Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning
Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9853519/ |
| _version_ | 1811319879010091008 |
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| author | Behnam Gholami Qingfeng Liu Mostafa El-Khamy Jungwon Lee |
| author_facet | Behnam Gholami Qingfeng Liu Mostafa El-Khamy Jungwon Lee |
| author_sort | Behnam Gholami |
| collection | DOAJ |
| description | Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which results in poor model performance. This calls for the need to have robust and data efficient deep learning models. In this work, we propose a deep learning approach called Multi-Expert Adversarial Regularization learning (MEAR) with limited computational overhead to improve the generalization and robustness of deep supervised learning models. The MEAR framework appends multiple classifier heads (experts) to the feature extractor of the legacy model. MEAR aims to learn the feature extractor in an adversarial fashion by leveraging complementary information from the individual experts as well as the ensemble of the experts to be more robust for an unseen test domain. We train state-of-the-art networks with MEAR for two important computer vision tasks, image classification and semantic segmentation. We compare MEAR to a variety of baselines on multiple benchmarks. We show that MEAR is competitive with other methods and more successful at learning robust features. |
| first_indexed | 2024-04-13T12:50:00Z |
| format | Article |
| id | doaj.art-30553d53b0a64e29b573dc36966e739d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T12:50:00Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-30553d53b0a64e29b573dc36966e739d2022-12-22T02:46:15ZengIEEEIEEE Access2169-35362022-01-0110850808509410.1109/ACCESS.2022.31967809853519Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised LearningBehnam Gholami0https://orcid.org/0000-0003-0338-1921Qingfeng Liu1Mostafa El-Khamy2https://orcid.org/0000-0001-9421-6037Jungwon Lee3SOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USASOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USASOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USASOC Cellular and Multimedia Research and Development Laboratory, Samsung Semiconductor Inc., San Diego, CA, USADeep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which results in poor model performance. This calls for the need to have robust and data efficient deep learning models. In this work, we propose a deep learning approach called Multi-Expert Adversarial Regularization learning (MEAR) with limited computational overhead to improve the generalization and robustness of deep supervised learning models. The MEAR framework appends multiple classifier heads (experts) to the feature extractor of the legacy model. MEAR aims to learn the feature extractor in an adversarial fashion by leveraging complementary information from the individual experts as well as the ensemble of the experts to be more robust for an unseen test domain. We train state-of-the-art networks with MEAR for two important computer vision tasks, image classification and semantic segmentation. We compare MEAR to a variety of baselines on multiple benchmarks. We show that MEAR is competitive with other methods and more successful at learning robust features.https://ieeexplore.ieee.org/document/9853519/Image classificationimage segmentationdata efficient learningrobust learningensemble methodsadversarial learning |
| spellingShingle | Behnam Gholami Qingfeng Liu Mostafa El-Khamy Jungwon Lee Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning IEEE Access Image classification image segmentation data efficient learning robust learning ensemble methods adversarial learning |
| title | Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning |
| title_full | Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning |
| title_fullStr | Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning |
| title_full_unstemmed | Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning |
| title_short | Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning |
| title_sort | multiexpert adversarial regularization for robust and data efficient deep supervised learning |
| topic | Image classification image segmentation data efficient learning robust learning ensemble methods adversarial learning |
| url | https://ieeexplore.ieee.org/document/9853519/ |
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