A3T: accuracy aware adversarial training
<div><p>Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons are still not fully understood. In this paper, we identify one cause of overfitting related to current practices of generating adversarial examples...
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| _version_ | 1864513533725638656 |
|---|---|
| author | Enes Altinisik (17725956) |
| author2 | Safa Messaoud (17725959) Husrev Taha Sencar (17725962) Sanjay Chawla (4254202) |
| author2_role | author author author |
| author_facet | Enes Altinisik (17725956) Safa Messaoud (17725959) Husrev Taha Sencar (17725962) Sanjay Chawla (4254202) |
| author_role | author |
| dc.creator.none.fl_str_mv | Enes Altinisik (17725956) Safa Messaoud (17725959) Husrev Taha Sencar (17725962) Sanjay Chawla (4254202) |
| dc.date.none.fl_str_mv | 2023-07-14T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10994-023-06341-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A3T_accuracy_aware_adversarial_training/24934944 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Software engineering Adversarial training Overftting in adversarial training Accuracy aware adversarial training |
| dc.title.none.fl_str_mv | A3T: accuracy aware adversarial training |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons are still not fully understood. In this paper, we identify one cause of overfitting related to current practices of generating adversarial examples from misclassified samples. We show that, following current practice, adversarial examples from misclassified samples results in harder-to-classify samples than the original ones. This leads to a complex adjustment of the decision boundary during training and hence overfitting. To mitigate this issue, we propose A3T, an accuracy aware AT method that generate adversarial example differently for misclassified and correctly classified samples. We show that our approach achieves better generalization while maintaining comparable robustness to state-of-the-art AT methods on a wide range of computer vision, natural language processing, and tabular tasks.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Machine Learning<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10994-023-06341-w" target="_blank">https://dx.doi.org/10.1007/s10994-023-06341-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bdb0c1695bf565f7d37ea5a3ac86655d |
| identifier_str_mv | 10.1007/s10994-023-06341-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24934944 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A3T: accuracy aware adversarial trainingEnes Altinisik (17725956)Safa Messaoud (17725959)Husrev Taha Sencar (17725962)Sanjay Chawla (4254202)Information and computing sciencesArtificial intelligenceSoftware engineeringAdversarial trainingOverftting in adversarial trainingAccuracy aware adversarial training<div><p>Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons are still not fully understood. In this paper, we identify one cause of overfitting related to current practices of generating adversarial examples from misclassified samples. We show that, following current practice, adversarial examples from misclassified samples results in harder-to-classify samples than the original ones. This leads to a complex adjustment of the decision boundary during training and hence overfitting. To mitigate this issue, we propose A3T, an accuracy aware AT method that generate adversarial example differently for misclassified and correctly classified samples. We show that our approach achieves better generalization while maintaining comparable robustness to state-of-the-art AT methods on a wide range of computer vision, natural language processing, and tabular tasks.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Machine Learning<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10994-023-06341-w" target="_blank">https://dx.doi.org/10.1007/s10994-023-06341-w</a></p>2023-07-14T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10994-023-06341-whttps://figshare.com/articles/journal_contribution/A3T_accuracy_aware_adversarial_training/24934944CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249349442023-07-14T03:00:00Z |
| spellingShingle | A3T: accuracy aware adversarial training Enes Altinisik (17725956) Information and computing sciences Artificial intelligence Software engineering Adversarial training Overftting in adversarial training Accuracy aware adversarial training |
| status_str | publishedVersion |
| title | A3T: accuracy aware adversarial training |
| title_full | A3T: accuracy aware adversarial training |
| title_fullStr | A3T: accuracy aware adversarial training |
| title_full_unstemmed | A3T: accuracy aware adversarial training |
| title_short | A3T: accuracy aware adversarial training |
| title_sort | A3T: accuracy aware adversarial training |
| topic | Information and computing sciences Artificial intelligence Software engineering Adversarial training Overftting in adversarial training Accuracy aware adversarial training |