Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenging scenario of Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) by introducing a new end-to-end deep learning framework called SHeDD . This problem is not entirely new but is gaining increasing attention within the research community . SSHDA methods are specifically designed to learn a target domain classifier by utilizing both labeled and unlabeled data from heterogeneous data sources, which is a complex task due to potential data distribution shifts between domains .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the scientific hypothesis related to Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) through the introduction of a new end-to-end deep learning framework called SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) . The framework is designed to learn a target domain classifier by utilizing both labeled and unlabeled data from heterogeneous data sources . The hypothesis focuses on addressing the challenges posed by SSHDA, which involves learning domain-invariant representations and relevant information for downstream tasks while dealing with modality heterogeneity across domains . The paper seeks to validate the effectiveness of this framework in handling SSHDA scenarios where source and target data differ in modality representation, such as in remote sensing applications .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper proposes a novel framework called SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) designed for Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) . This framework is tailored to learn a target domain classifier by utilizing both labeled and unlabeled data from heterogeneous data sources . SHeDD aims to disentangle domain-invariant representations from domain-specific information to facilitate cross-modality transfer . Additionally, SHeDD incorporates an augmentation-based consistency regularization mechanism that leverages pseudo-labels on unlabeled target samples to enhance generalization on the target domain .
In contrast to existing methods that rely on pre-trained models for standard modalities like RGB imagery and text data, limiting their applicability in scenarios with non-standard sensor data, SHeDD is an end-to-end deep learning framework specifically designed for SSHDA . It addresses the challenge of SSHDA by explicitly seeking domain-invariant representations crucial for downstream tasks, especially in scenarios where source and target data differ in modality representation . The framework optimizes the neural network to classify and align embedding representations from different heterogeneous domains simultaneously using cross-entropy and domain critic discrimination based on Wasserstein distance .
Moreover, the paper explores the use of semi-supervised learning strategies such as FlexMatch and various augmentation techniques to enhance model performance in data-scarce environments . In the medium term, the authors plan to extend the framework to a multi-source domain adaptation setting to enable the use of multiple heterogeneous domains as source data, potentially leading to a more robust classifier and improved performance on the target domain . The proposed framework aims to overcome the challenges posed by heterogeneous data modalities and distribution shifts between domains, offering a promising approach for addressing SSHDA scenarios effectively . The proposed framework, SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement), offers several key characteristics and advantages compared to previous methods outlined in the paper :
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Tailored for Heterogeneous Domain Adaptation: SHeDD is specifically designed for Semi-Supervised Heterogeneous Domain Adaptation (SSHDA), addressing scenarios where source and target data differ in modality representation, such as in remote sensing with various acquisition modes, spectral characteristics, and spatial resolutions .
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Disentanglement of Domain-Invariant Representations: SHeDD focuses on disentangling domain-invariant representations from domain-specific information to facilitate cross-modality transfer, crucial for downstream tasks in SSHDA settings .
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Augmentation-Based Consistency Regularization: The framework incorporates an augmentation-based consistency regularization mechanism that leverages pseudo-labels on unlabeled target samples to enhance generalization on the target domain, improving model performance in data-scarce environments .
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End-to-End Deep Learning Framework: Unlike existing methods that rely on pre-trained models for standard modalities like RGB imagery and text data, SHeDD is an end-to-end deep learning framework specifically designed for SSHDA, optimizing neural networks to classify and align embedding representations from different heterogeneous domains simultaneously .
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Performance Improvement: Empirical evaluations demonstrate that SHeDD systematically outperforms competing approaches in terms of F1-Score, showcasing gains of over 2 points compared to strategies relying solely on target information, regardless of the amount of labeled target samples .
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Flexibility and Robustness: SHeDD's structural design allows for modeling a wide range of heterogeneous data transfer scenarios effectively, making it flexible and robust in handling diverse data modalities and distribution shifts between domains .
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Future Directions: In the medium term, the authors plan to extend the framework to a multi-source domain adaptation setting, enabling the use of multiple heterogeneous domains as source data, potentially leading to a more robust classifier and improved performance on the target domain .
Overall, SHeDD's emphasis on disentanglement, augmentation-based regularization, end-to-end deep learning, and superior performance in SSHDA scenarios highlight its advancements and effectiveness in addressing the challenges posed by heterogeneous data modalities and distribution shifts between domains.
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research efforts exist in the field of Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) . Noteworthy researchers in this field include Cassio F. Dantas, Raffaele Gaetano, and Dino Ienco . Other researchers who have contributed to this area include Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu, Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, Kate Saenko, and many more .
The key to the solution mentioned in the paper involves the development of an end-to-end deep learning framework called SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) . This framework is specifically designed to learn a target domain classifier by leveraging both labelled and unlabelled data from heterogeneous data sources. SHeDD aims to effectively disentangle domain-invariant representations from domain-specific information, which is crucial for enhancing cross-modality transfer. Additionally, SHeDD incorporates an augmentation-based consistency regularization mechanism that utilizes reliable pseudo-labels on unlabelled target samples to improve its generalization ability on the target domain .
How were the experiments in the paper designed?
The experiments in the paper were designed by conducting evaluations on two remote sensing benchmarks, RESISC45-Euro and EuroSat-MS-SAR, with varying amounts of labelled target samples . The experiments focused on two transfer tasks: (RGB → MS) and (MS → RGB), where the domains differed in terms of radiometric content and spatial resolution . The methods were implemented in Pytorch and the experiments were carried out on a workstation equipped with an Intel(R) Xeon(R) Gold 6226R CPU, 377Gb of RAM, and four RTX3090 GPU . The results of the experiments were reported in terms of F1-Score, and each experiment was repeated five times to provide average and standard deviation results .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the RESISC45-Euro and EuroSat-MS-SAR benchmarks . The methods implemented in the study are available in Pytorch , indicating that the code is open source and accessible for further exploration and implementation.
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The research work introduces a new end-to-end deep learning framework, SHeDD, specifically designed for Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) . The framework aims to learn a target domain classifier by utilizing both labelled and unlabelled data from heterogeneous data sources . The experiments conducted on remote sensing benchmarks, RESISC45-Euro and EuroSat-MS-SAR, demonstrate the effectiveness of SHeDD compared to baseline and state-of-the-art approaches . The results are reported in terms of F1-Score, considering different amounts of labelled target samples, and show the performance of various methods . Additionally, the paper highlights the importance of addressing data distribution shifts between source and target domains, especially in scenarios involving non-standard sensor data, such as those in the medical and remote sensing fields . The empirical evaluations on heterogeneous data in terms of acquisition modes and spectral/spatial resolutions validate the quality of SHeDD . Overall, the experiments and results provide robust evidence supporting the effectiveness of the proposed framework for Semi-Supervised Heterogeneous Domain Adaptation .
What are the contributions of this paper?
The paper "Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling" makes several key contributions in the field of domain adaptation:
- Introduction of SHeDD Framework: The research work introduces the SHeDD framework, an end-to-end deep learning framework specifically designed for learning a target domain classifier by utilizing both labelled and unlabelled data from heterogeneous data sources .
- Addressing SSHDA Setting: The paper addresses the challenging scenario of Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) by focusing on disentangling domain-invariant representations from domain-specific information, which is crucial for effective cross-modality transfer .
- Incorporation of Consistency Regularization: The framework incorporates an augmentation-based consistency regularization mechanism that leverages reliable pseudo-labels on unlabelled target samples to enhance generalization on the target domain .
- Performance Evaluation: Through empirical evaluations on remote sensing benchmarks, the paper demonstrates the effectiveness of the SHeDD framework in handling heterogeneous data in terms of acquisition modalities .
- Comparison with Existing Approaches: The paper highlights the limitations of existing methods designed for homogeneous domains and proposes a novel approach based on feature disentanglement to address the challenges posed by heterogeneous data representations .
- Ablation Study: The research conducts an ablation study to comprehensively assess the components of the SHeDD framework, showing the positive impact of enforcing disentanglement between domain-invariant and domain-specific features, as well as the benefits of leveraging unlabelled target data and consistency regularization .
What work can be continued in depth?
Further research in this field can delve deeper into exploring the impact of various augmentation techniques on consistency regularization and pseudo-labeling to enhance model performance in data-scarce environments . Additionally, extending the framework towards a multi-source domain adaptation setting could be beneficial, enabling the utilization of multiple heterogeneous domains as a source of data. This extension has the potential to create a more robust classifier and potentially improve performance on the target domain .