Description:Brief Description of the Drawings

Generally, the present disclosure relates to machine learning. Particularly, the present disclosure relates to robust cross-dataset machine learning model deployment.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
In recent years, machine learning has witnessed significant advancements, transforming industries through innovative applications ranging from predictive analytics to autonomous systems. Within this realm, the deployment of machine learning models across diverse datasets stands as a critical challenge. This challenge stems from the inherent variability and specificity of data across different domains, which can significantly impact the performance of machine learning models. Consequently, the development of methods for robust cross-dataset machine learning model deployment has garnered substantial attention.
One prevalent approach involves the use of feature extractors. Feature extractors are designed to learn domain-invariant features from datasets, facilitating the generalization of machine learning models across different datasets. By focusing on features that are consistent across domains, models aim to maintain high levels of accuracy even when deployed in environments dissimilar to those they were trained in.
Another approach centers around domain adaptation networks. These networks aim to minimize domain discrepancies by aligning the distribution of features between source and target datasets. Through this alignment, models can better adapt to new domains, enhancing their performance on datasets that were not part of their initial training.
Further, the employment of domain discriminators represents an additional strategy. These discriminators distinguish between domain-invariant features of source and target datasets, playing a pivotal role in identifying domain-specific characteristics that might hinder cross-dataset deployment.
Moreover, adversarial learning frameworks have emerged as a potent method for training feature extractors and domain adaptation networks. In these frameworks, the domain adaptation network and the domain discriminator iteratively optimize their parameters in opposition to each other. This minimax adversarial learning process fosters a competitive environment that enhances the robustness and adaptability of machine learning models.
Despite these advancements, challenges persist. For instance, the effective learning of domain-invariant features requires sophisticated algorithms capable of identifying and isolating these features amidst a plethora of domain-specific characteristics. Additionally, the minimization of domain discrepancy measures demands precise metrics that accurately reflect the differences between source and target datasets. Moreover, the iterative optimization process in adversarial learning frameworks necessitates careful balancing to prevent the dominance of either the domain adaptation network or the domain discriminator, which could compromise model performance.
In light of the above discussion, there exists an urgent need for solutions that overcome the challenges associated with conventional systems and techniques for robust cross-dataset machine learning model deployment.

Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
In an aspect, the present disclosure aims to provide a method for robust cross-dataset machine learning model deployment. The method involves obtaining a source dataset and a target dataset where the former includes labeled data and the latter comprises unlabeled data. A feature extractor is employed to learn domain-invariant features from both datasets. Furthermore, a domain adaptation network is utilized to reduce a domain discrepancy measure between the learned domain-invariant features of the source and target datasets. Additionally, a domain discriminator is applied to distinguish between the domain-invariant features of the source and target datasets. The feature extractor and the domain adaptation network undergo training within a minimax adversarial learning framework, wherein the domain adaptation network and the domain discriminator iteratively optimize their parameters in opposition to each other.
Moreover, the domain adaptation network performs auxiliary tasks, including domain confusion or reconstruction of domain-specific input samples. A gradient reversal layer is employed to align gradients used to update the domain adaptation network with the objectives of the feature extractor. Depending on the availability of labeled data in the target dataset, an unsupervised, semi-supervised, or weakly supervised learning module is included. The effectiveness of adversarial domain adaptation is evaluated through comprehensive assessment and benchmarking across multiple datasets and application domains. In scenarios of unsupervised domain adaptation, the domain adaptation model undergoes training only on the source dataset and unlabeled target dataset without access to target domain labels.
In another aspect, the disclosure provides a system for robust cross-dataset machine learning model deployment. This system consists of a data storage component for storing a source dataset with labeled data and a target dataset with unlabeled data. A feature extraction component, including one or more processors, extracts domain-invariant features from the source and target datasets. A domain adaptation component, comprising a neural network, is tasked with minimizing a domain discrepancy measure between the domain-invariant features of the source dataset and the target dataset. A domain discriminator component differentiates the domain-invariant features derived from the source and target datasets. An adversarial training component conducts a minimax game between the domain adaptation component and the domain discriminator component, adjusting parameters of both components in opposition to each other. A controller processor executes instructions associated with operations of the feature extraction component, the domain adaptation component, the domain discriminator component, and the adversarial training component. The domain adaptation component includes a gradient reversal layer that aligns gradients used to update the network with the objectives of the feature extractor.

Field of the Invention

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a method (100) relates to a process designed for robust cross-dataset machine learning model deployment, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a block diagram of a system for robust cross-dataset machine learning model deployment, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a detailed process flow for implementing adversarial domain adaptation techniques for machine learning models when deployed across datasets with varying characteristics, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates the architecture supporting the workflow by defining each phase in a more structured manner, in accordance with the embodiments of the present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a method (100) relates to a process designed for robust cross-dataset machine learning model deployment, in accordance with the embodiments of the present disclosure. This process facilitates the utilization of machine learning models across different datasets, enhancing their adaptability and performance in varied domains. The method (100) commences with a step (102) obtaining of a source dataset and a target dataset. The source dataset is characterized by the inclusion of labeled data, whereas the target dataset comprises unlabeled data. The step (102) is fundamental for preparing the datasets for subsequent domain-invariant feature learning and adaptation processes. Employing a feature extractor forms the next step (104). The feature extractor is tasked with learning domain-invariant features from the source and target datasets. Such features are essential for ensuring the model's generalization capability across different datasets. The learning of domain-invariant features facilitates the model's robust performance, notwithstanding the variability inherent in different domains. The use of a domain adaptation network follows in step (106). This network is utilized to minimize a domain discrepancy measure between the learned domain-invariant features of the source dataset and the target dataset. By reducing the domain discrepancy, the model's adaptability and performance on the target dataset are significantly enhanced. Subsequently, in step (108) the application of a domain discriminator is undertaken. The domain discriminator differentiates between the domain-invariant features of the source dataset and the target dataset. This differentiation is pivotal for refining the model's ability to generalize across datasets by recognizing and adjusting to domain-specific characteristics. In step (110), the training of the feature extractor and the domain adaptation network in a minimax adversarial learning framework is conducted. In this framework, the domain adaptation network and the domain discriminator iteratively optimize their parameters in opposition to each other. This adversarial approach promotes the development of more robust and adaptable machine learning models, capable of effective deployment across diverse datasets.
In an embodiment, the domain adaptation network is configured to perform auxiliary tasks including domain confusion or reconstruction of domain-specific input samples. The inclusion of these auxiliary tasks plays a crucial role in enhancing the adaptability and performance of the machine learning model across different datasets. Domain confusion tasks aim to make the domain adaptation network less sensitive to the differences between source and target datasets, thereby improving the generalization of the model. On the other hand, the reconstruction of domain-specific input samples facilitates the model's ability to recognize and adapt to the unique characteristics of each dataset. This approach not only improves the robustness of the domain adaptation process but also ensures that the model remains effective even in the face of significant variability across datasets. The implementation of such auxiliary tasks represents a significant advancement in the field of domain adaptation, providing a more nuanced and effective method for achieving cross-dataset model deployment.
In another embodiment, the method further comprises employing a gradient reversal layer to align gradients used to update the domain adaptation network with the objectives of the feature extractor. This gradient reversal layer acts as a pivotal component in the adversarial training framework, ensuring that the updates made to the domain adaptation network actively contribute to the overall goal of domain-invariant feature learning. By reversing the direction of the gradients, the gradient reversal layer encourages the domain adaptation network to learn features that are beneficial for the feature extractor's objective of identifying domain-invariant features. This alignment of objectives between the domain adaptation network and the feature extractor significantly enhances the efficacy of the adversarial training process, leading to more robust and adaptable machine learning models. The gradient reversal layer thus represents a sophisticated technique for optimizing the training process, ensuring that the adversarial components work in harmony towards the common goal of effective domain adaptation.
In a further embodiment, the method incorporates an unsupervised, semi-supervised, or weakly supervised learning module based on the availability of labeled data in the target dataset. This flexible approach to learning allows the machine learning model to adapt to the specific conditions of the target dataset, making the most of whatever labeled data may be available. In scenarios where labeled data is scarce or entirely absent, unsupervised learning techniques are utilized, relying solely on the unlabeled data in the target dataset. Semi-supervised and weakly supervised learning techniques, on the other hand, leverage a limited amount of labeled data to guide the learning process, striking a balance between the robustness of supervised learning and the flexibility of unsupervised learning. This adaptability in the learning approach ensures that the model can be effectively deployed across a wide range of datasets, enhancing its utility in diverse application domains.
In yet another embodiment, the effectiveness of the adversarial domain adaptation is evaluated by a comprehensive assessment and benchmarking across multiple datasets and application domains. This evaluation process is essential for validating the adaptability and performance of the machine learning model in real-world scenarios. By subjecting the model to rigorous testing across various datasets, researchers and practitioners can gain a clear understanding of its strengths and limitations, enabling targeted improvements and optimizations. The comprehensive assessment also provides valuable insights into the model's generalization capabilities, highlighting its potential applicability in different domains. Benchmarking against established datasets and application domains further ensures that the model meets the high standards required for practical deployment, cementing its place as a valuable tool in the field of machine learning.
In an embodiment, the domain adaptation model is trained only on the source dataset and unlabeled target dataset without access to target domain labels in unsupervised domain adaptation scenarios. This approach to training is particularly advantageous in situations where labeled data for the target dataset is unavailable or prohibitively expensive to obtain. By relying solely on the source dataset's labeled data and the target dataset's unlabeled data, the model leverages the intrinsic patterns and structures within the data to bridge the gap between the two domains. This unsupervised domain adaptation strategy is crucial for enabling the deployment of machine learning models in new and unexplored domains, where the collection of labeled data may not be feasible. The ability to adapt to new datasets without the need for labeled target data significantly expands the model's applicability and utility across a wide range of real-world scenarios.
The term "system" as used throughout the present disclosure relates to an assembly configured for robust cross-dataset machine learning model deployment. This system facilitates the utilization of machine learning models across diverse datasets by enhancing their adaptability and performance in different domains.
The term "data storage component" relates to a device or assembly configured to store digital data. In the context of the disclosed system, the data storage component (202) is specifically configured to house a source dataset endowed with labeled data alongside a target dataset composed of unlabeled data. This configuration is pivotal for the preparatory phase of cross-dataset deployment, ensuring that both labeled and unlabeled data are readily available for processing and analysis.
The term "feature extraction component" refers to a subsystem comprising one or more processors, dedicated to extracting meaningful information from data. Within the disclosed system, the feature extraction component (204) is tasked with extracting domain-invariant features from both the source and target datasets. By isolating features that are consistent across different domains, this component significantly contributes to the model's ability to generalize across varied datasets.
The term "domain adaptation component" pertains to a subsystem inclusive of a neural network designed to align data distributions. The domain adaptation component (206) in the system is configured to minimize a domain discrepancy measure between the domain-invariant features of the source dataset and the target dataset. This minimization aids in adapting the machine learning model to perform effectively across datasets from different domains.
The term "domain discriminator component" denotes a subsystem configured to differentiate features based on their domain of origin. In the system, the domain discriminator component (208) is tasked with distinguishing between the domain-invariant features derived from the source dataset and those from the target dataset. This differentiation plays a critical role in refining the model's generalization capabilities.
The term "adversarial training component" describes a subsystem designed to facilitate a competitive training process. The adversarial training component (210) in the disclosed system is responsible for conducting a minimax game between the domain adaptation component (206) and the domain discriminator component (208). Through this process, the adversarial training component (210) adjusts the parameters of both components in opposition to each other, enhancing the robustness and adaptability of the machine learning model.
Additionally, the term "controller processor" refers to a central processing unit tasked with overseeing and executing system operations. The controller processor (212) in the system is configured to execute instructions associated with the operations of the feature extraction component (204), the domain adaptation component (206), the domain discriminator component (208), and the adversarial training component (210). This centralized control ensures coherent and efficient system operation, facilitating the seamless deployment of machine learning models across datasets.
FIG. 2 illustrates a block diagram of a system for robust cross-dataset machine learning model deployment, in accordance with the embodiments of the present disclosure. The system comprises a data storage component (202), a feature extraction component (204), a domain adaptation component (206), a domain discriminator component (208), an adversarial training component (210), and a controller processor (212). The data storage component (202) is configured to store the source dataset, which contains labeled data, and the target dataset, which contains unlabeled data. Said data storage component (202) facilitates the retrieval and storage of data necessary for subsequent processing by other components of the system. The feature extraction component (204) is comprised of one or more processors and is configured to extract domain-invariant features from the source and target datasets. Such feature extraction component (204) enables the system to identify and utilize features common to both datasets, enhancing the adaptability of the machine learning model. Adjacent to the feature extraction component (204) is the domain adaptation component (206), which includes a neural network. Such domain adaptation component (206) is configured to minimize a domain discrepancy measure between the domain-invariant features of the source dataset and the target dataset, thereby enabling the model to perform accurately on the target dataset. The domain discriminator component (208) is configured to differentiate between the domain-invariant features derived from the source dataset and the target dataset. Such differentiation by said domain discriminator component (208) is essential for enhancing the robustness of the machine learning model. Furthermore, the adversarial training component (210) is configured to conduct a minimax game between the domain adaptation component (206) and the domain discriminator component (208). Parameters of both components are adjusted in opposition to each other by said adversarial training component (210), ensuring the iterative optimization necessary for robust model deployment. Additionally, the controller processor (212) is configured to execute instructions associated with the operations of the feature extraction component (204), the domain adaptation component (206), the domain discriminator component (208), and the adversarial training component (210). Such controller processor (212) coordinates the overall function of the system, ensuring that the deployment of the machine learning model is executed efficiently and effectively.
In an embodiment of the system for robust cross-dataset machine learning model deployment, an enhancement to the domain adaptation component (206) is disclosed. Specifically, the domain adaptation component (206) is augmented with the inclusion of a gradient reversal layer. This gradient reversal layer serves a critical function in the system's operation by aligning the gradients used to update the neural network within the domain adaptation component (206) with the objectives of the feature extraction component (204).
The primary purpose of the gradient reversal layer is to facilitate the learning of domain-invariant features by inverting the direction of the gradient during the backpropagation process. This inversion is strategic, as it compels the domain adaptation component (206) to focus on minimizing domain discrepancies in a manner that is beneficial to the feature extraction process. Consequently, the domain adaptation component (206) learns representations that are not only conducive to domain adaptation but also aligned with the feature extraction goals of identifying domain-invariant characteristics. This alignment is particularly crucial in the context of adversarial training, where the domain adaptation component (206) and the domain discriminator component (208) engage in a learning process characterized by iterative optimization in opposition to each other. The gradient reversal layer ensures that despite this adversarial setup, the updates made to the domain adaptation component (206) do not detract from the overarching objective of extracting domain-invariant features, thereby maintaining the coherence and efficacy of the model's learning strategy.
Furthermore, the inclusion of the gradient reversal layer underscores the system's innovative approach to addressing the challenges of cross-dataset machine learning model deployment. By leveraging such sophisticated mechanisms, the system (200) significantly enhances its adaptability and performance across diverse datasets, illustrating a marked advancement in the field of machine learning. This embodiment exemplifies the system's commitment to optimizing the interaction between its components to achieve robust, domain-agnostic model deployment.
FIG. 3 illustrates a detailed process flow for implementing adversarial domain adaptation techniques for machine learning models when deployed across datasets with varying characteristics, in accordance with the embodiments of the present disclosure. To illustrate this process with an example, consider a machine learning model developed to recognize and classify images of vehicles from a dataset collected in a European country (source dataset). The goal is to deploy this model in an Asian country (target dataset) where the background scenery, vehicle models, and lighting conditions may differ significantly. The process starts by identifying these source and target datasets, acknowledging that the European dataset contains images with specific characteristics of that region, while the Asian dataset includes images with distinct regional attributes. The model initially trained on the European dataset needs to generalize well to the Asian dataset without being retrained from scratch. Three primary adaptation techniques are suggested: Domain Adversarial Neural Network (DANN), Generative Adversarial Network (GAN), and Contrastive Domain Adaptation. The DANN method employs adversarial training to make the features from the source and target datasets indistinguishable to the model, thereby improving its generalization capabilities. The GAN technique uses a generator and discriminator to produce data that is similar to the target dataset, thus enabling the model to perform better on the target data. Contrastive Domain Adaptation focuses on reducing the difference between representations of the source and target data. For an instance, chosen technique is the Domain Adversarial Neural Network (DANN) that undergoes training where it learns to minimize the difference in feature distribution between the European and Asian images. The training is done through an adversarial process where one part of the network attempts to classify the images correctly while another part tries to make the source and target dataset features indistinguishable from one another. After training with DANN, the model is evaluated to assess how well it classifies the vehicles in the Asian dataset, despite being trained on European data. If the performance metrics meet the set criteria, the model is ready for deployment in the Asian setting.
FIG. 4 illustrates the architecture supporting the workflow by defining each phase in a more structured manner, in accordance with the embodiments of the present disclosure. Taking the vehicle classification example forward, the architecture begins with data sources, identifying the specific European and Asian datasets. During adversarial training, a model is trained using DANN, which involves a feature extractor that makes the European and Asian vehicle images feature representations similar. Once trained, the model is deployed in a server responsible for processing live traffic camera feeds in the Asian country. The model's purpose is to accurately classify vehicles in real-time, regardless of the regional differences in vehicle design or environmental conditions. The final phase is monitoring. The performance of the model is continuously assessed to ensure that it maintains a high level of accuracy and can adapt to any new patterns or changes in the dataset, such as the introduction of a new vehicle model that was not present in the training data. Adaptation effectiveness is assessed by how well the model continues to perform over time, and adjustments are made if performance dips, possibly by reinitiating the adaptation cycle with updated datasets.
In an embodiment, adversarial domain adaptation techniques enables that machine learning models remain robust when encountering shifts in data domains. The adversarial domain adaptation enhance resilience of models by aligning the features of different datasets, which allows for better performance across various settings. The alignment process keeps domain-specific and domain-invariant features intact, enabling models to function effectively despite the distinct characteristics of each dataset. Adversarial domain adaptation overcome general limitation of dataset bias, which presents a challenge in machine learning as models trained on one dataset often underperform when applied to another due to the differences in data distribution. Regarding computational resources, training large deep learning models is resource intensive. Adversarial domain adaptation saves on computational costs by optimizing pre-existing models for new datasets without the need for retraining from scratch. Further, the present disclosure enables effectively transfer knowledge from one domain to another domain by utilizing methods like maximum mean discrepancy, correlation alignment, and adversarial training to minimize the distribution mismatch between domains. Present system (as disclosed in disclosure) provides various advantages such as prevents performance decline due to domain shifts by aligning different data distributions while preserving domain-specific and invariant features, and it enhances the generalizability of models. Further, dataset bias can be minimized to enable fairer outcomes across various datasets. By learning domain-invariant features, the models maintain consistent feature distribution regardless of the domain of the input data. Additionally, iterative adaptation offers a way to adjust models to new datasets over time, especially beneficial in scenarios with limited labeled data, where models are trained on labeled source data and then adjusted to unlabeled data in the target domain.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We Claims

A method (100) for robust cross-dataset machine learning model deployment, comprising:
a. obtaining a source dataset and a target dataset, wherein the source dataset comprises labeled data and the target dataset comprises unlabeled data;
b. employing a feature extractor to learn domain-invariant features from the source and target datasets;
c. using a domain adaptation network to minimize a domain discrepancy measure between the learned domain-invariant features of the source dataset and the target dataset;
d. applying a domain discriminator to differentiate between the domain-invariant features of the source dataset and the target dataset; and
e. training the feature extractor and the domain adaptation network in a minimax adversarial learning framework where the domain adaptation network and the domain discriminator iteratively optimize their parameters in opposition to each other.
The method (100) of claim 1, wherein the domain adaptation network is configured to perform auxiliary tasks including domain confusion or reconstruction of domain-specific input samples.
The method (100) of claim 1, further comprising employing a gradient reversal layer to align gradients used to update the domain adaptation network with the objectives of the feature extractor.
The method (100) of claim 1, further comprising unsupervised, semi-supervised, or weakly supervised learning module based on the availability of labeled data in the target dataset.
The method (100) of claim 1, wherein the effectiveness of the adversarial domain adaptation is evaluated by a comprehensive assessment and benchmarking across multiple datasets and application domains.
The method (100) of claim 1, where the domain adaptation model is trained only on the source dataset and unlabeled target dataset without access to target domain labels in unsupervised domain adaptation scenarios.
A system (200) for robust cross-dataset machine learning model deployment, comprising:
a. a data storage component (202) configured to store a source dataset with labeled data and a target dataset with unlabeled data;
b. a feature extraction component (204) comprising one or more processors and configured to extract domain-invariant features from the source and target datasets;
c. a domain adaptation component (206) comprising a neural network, wherein the neural network is configured to minimize a domain discrepancy measure between the domain-invariant features of the source dataset and the target dataset;
d. a domain discriminator component (208) configured to differentiate the domain-invariant features derived from the source dataset and the target dataset;
e. an adversarial training component (210) configured to conduct a minimax game between the domain adaptation component (206) and the domain discriminator component (208), wherein the adversarial training component (210) adjusts parameters of both components in opposition to each other; and
f. a controller processor (212) configured to execute instructions associated with operations of the feature extraction component (204), the domain adaptation component (206), the domain discriminator component (208), and the adversarial training component (210).
The system (200) of claim 4, where the domain adaptation component (206) includes a gradient reversal layer that aligns gradients used to update the network with the objectives of the feature extractor.

MACHINE LEARNING DEPLOYMENT THROUGH ADVERSARIAL DOMAIN ADAPTATION METHODS

Disclosed is a method for robust cross-dataset machine learning model deployment. The method comprises obtaining a source dataset and a target dataset, wherein the source dataset comprises labeled data and the target dataset comprises unlabeled data. The method further includes employing a feature extractor to learn domain-invariant features from the source and target datasets. A domain adaptation network is used to minimize a domain discrepancy measure between the learned domain-invariant features of the source dataset and the target dataset. Additionally, the method involves applying a domain discriminator to differentiate between the domain-invariant features of the source dataset and the target dataset. The feature extractor and the domain adaptation network are trained in a minimax adversarial learning framework where the domain adaptation network and the domain discriminator iteratively optimize their parameters in opposition to each other.

, Claims:I/We Claims

A method (100) for robust cross-dataset machine learning model deployment, comprising:
a. obtaining a source dataset and a target dataset, wherein the source dataset comprises labeled data and the target dataset comprises unlabeled data;
b. employing a feature extractor to learn domain-invariant features from the source and target datasets;
c. using a domain adaptation network to minimize a domain discrepancy measure between the learned domain-invariant features of the source dataset and the target dataset;
d. applying a domain discriminator to differentiate between the domain-invariant features of the source dataset and the target dataset; and
e. training the feature extractor and the domain adaptation network in a minimax adversarial learning framework where the domain adaptation network and the domain discriminator iteratively optimize their parameters in opposition to each other.
The method (100) of claim 1, wherein the domain adaptation network is configured to perform auxiliary tasks including domain confusion or reconstruction of domain-specific input samples.
The method (100) of claim 1, further comprising employing a gradient reversal layer to align gradients used to update the domain adaptation network with the objectives of the feature extractor.
The method (100) of claim 1, further comprising unsupervised, semi-supervised, or weakly supervised learning module based on the availability of labeled data in the target dataset.
The method (100) of claim 1, wherein the effectiveness of the adversarial domain adaptation is evaluated by a comprehensive assessment and benchmarking across multiple datasets and application domains.
The method (100) of claim 1, where the domain adaptation model is trained only on the source dataset and unlabeled target dataset without access to target domain labels in unsupervised domain adaptation scenarios.
A system (200) for robust cross-dataset machine learning model deployment, comprising:
a. a data storage component (202) configured to store a source dataset with labeled data and a target dataset with unlabeled data;
b. a feature extraction component (204) comprising one or more processors and configured to extract domain-invariant features from the source and target datasets;
c. a domain adaptation component (206) comprising a neural network, wherein the neural network is configured to minimize a domain discrepancy measure between the domain-invariant features of the source dataset and the target dataset;
d. a domain discriminator component (208) configured to differentiate the domain-invariant features derived from the source dataset and the target dataset;
e. an adversarial training component (210) configured to conduct a minimax game between the domain adaptation component (206) and the domain discriminator component (208), wherein the adversarial training component (210) adjusts parameters of both components in opposition to each other; and
f. a controller processor (212) configured to execute instructions associated with operations of the feature extraction component (204), the domain adaptation component (206), the domain discriminator component (208), and the adversarial training component (210).
The system (200) of claim 4, where the domain adaptation component (206) includes a gradient reversal layer that aligns gradients used to update the network with the objectives of the feature extractor.

MACHINE LEARNING DEPLOYMENT THROUGH ADVERSARIAL DOMAIN ADAPTATION METHODS