Adaptive L2 Regularization in Person Re-Identification
This work addresses the need for automated regularization tuning in person re-identification, offering an incremental improvement over existing methods.
The paper tackles the problem of manually tuning regularization factors in person re-identification by introducing an adaptive L2 regularization mechanism that updates factors through backpropagation, achieving state-of-the-art performance on the MSMT17 dataset.
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.