Adaptive L2 Regularization in Person Re-Identification

Abstract

We introduce an adaptive L2 regularization mechanism in the setting of person re-identification. In the literature, it is common practice to utilize hand-picked regularization factors which remain constant throughout the training procedure. Unlike existing approaches, the regularization factors in our proposed method are updated adaptively through backpropagation. This is achieved by incorporating trainable scalar variables as the regularization factors, which are further fed into a scaled hard sigmoid function. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets validate the effectiveness of our framework. Most notably, we obtain state-of-the-art performance on MSMT17, which is the largest dataset for person re-identification. Source code is publicly available at https://github.com/nixingyang/AdaptiveL2Regularization.

arXiv commentAccepted at ICPR 2020

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• Adam: A Method for Stochastic Optimization
• Decoupled Weight Decay Regularization
• In Defense of the Triplet Loss for Person Re-Identification
• Improving Person Re-identification by Attribute and Identity Learning
• Random Erasing Data Augmentation
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
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Cited by in corpus (3)

• FPB: Feature Pyramid Branch for Person Re-Identification
• Fine-Grained Re-Identification
• Pose Invariant Person Re-Identification using Robust Pose-transformation GAN