Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation
| العنوان: | Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation |
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| المؤلفون: | Sandipan Choudhuri, Suli Adeniye, Arunabha Sen, Hemanth Venkateswara |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG) |
| الوصف: | The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation alleviates this problem of procuring a labeled dataset with identical label space assumptions and addresses a more practical scenario where the source label set subsumes the target label set. This, however, presents a few additional obstacles during adaptation. Samples with categories private to the source domain thwart relevant knowledge transfer and degrade model performance. In this work, we try to address these issues by coupling variational information and adversarial learning with a pseudo-labeling technique to enforce class distribution alignment and minimize the transfer of superfluous information from the source samples. The experimental findings in numerous cross-domain classification tasks demonstrate that the proposed technique delivers superior and comparable accuracy to existing methods. Accepted in the 56th Asilomar Conference on Signals, Systems, and Computers, 2022 |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c21e7afd15d38acb87ab49392318b28a http://arxiv.org/abs/2212.01590 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....c21e7afd15d38acb87ab49392318b28a |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |