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Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

7 Apr 2021CVPR 2021 1arXiv:2104.02862archive 2025-07-28

Xudong Tian, Zhizhong Zhang, Shaohui Lin, Yanyun Qu, Yuan Xie, Lizhuang Ma

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to view-changes by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual

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ChannelCompress FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification/reid/models/newresnet.py official repository ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only · 15fb0c1e7170d4ea · report
VIB FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification/reid/models/newresnet.py official repository ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only · 75d8018047cdaafe · report
weights_init_classifier FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification/reid/models/newresnet.py official repository unverified no licence file found · pointer only · 7ea46327a3236792 · report
weights_init_kaiming FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification/reid/models/newresnet.py official repository unverified no licence file found · pointer only · 6a8e7f94d2d45588 · report

Tasks

Cross-Modal Person Re-IdentificationCross-Modal Person Re-IdentificationCross-Modality Person Re-identificationMULTI-VIEW LEARNINGPerson Re-IdentificationRepresentation Learning

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