Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-Grained Recognition

Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization capability. To this end, we propose a novel Stochastic Partial Swap (SPS) scheme to address this issue. Our method performs element-wise swapping for partial features between samples to inject noise during training. It equips a regularization effect similar to Dropout, which promotes more neurons to represent the concepts. Furthermore, it also exhibits other advantages: 1) suppressing over-activation to some part patterns to improve feature representativeness, and 2) enriching pattern combination and simulating noisy cases to enhance classifier generalization. We verify the effectiveness of our approach through comprehensive experiments across four network backbones and three fine-grained datasets. Moreover, we demonstrate its ability to complement high-level representations, allowing a simple model to achieve performance comparable to the top-performing technologies in fine-grained recognition, indoor scene recognition, and material recognition while improving model interpretability.