Revisiting Stochastic Learning for Generalizable Person Re-identification

Generalizable person re-identification aims to achieve a well generalization capability on target domains without accessing target data. Existing methods focus on suppressing domain-specific information or simulating unseen environments by meta-learning strategies, which could damage the capture ability on fine-grained visual patterns or lead to overfitting issues by the repetitive training of episodes. In this paper, we revisit the stochastic behaviors from two different perspectives: 1) Stochastic splitting-sliding sampler. It splits domain sources into approximately equal sample-size subsets and selects several subsets from various sources by a sliding window, forcing the model to step out of local minimums under stochastic sources. 2) Variance-varying gradient dropout. Gradients in parts of network are also selected by a sliding window and multiplied by binary masks generated from Bernoulli distribution, making gradients in varying variance and preventing the model from local minimums. By applying these two proposed stochastic behaviors, the model achieves a better generalization performance on unseen target domains without any additional computation costs or auxiliary modules. Extensive experiments demonstrate that our proposed model is effective and outperforms state-of-the-art methods on public domain generalizable person Re-ID benchmarks.