Large-margin Softmax loss using synthetic virtual class

The primary challenge of large-margin learning lies in designing classifiers with strong discriminative power. Although existing large margin methods have achieved success in various classification tasks, they often suffer from weak task generalization and imbalanced handling of easy and hard samples. In this paper, we propose a margin adaptive synthetic virtual Softmax loss (SV-Softmax), which dynamically generates virtual prototypes by synthesizing embedded features and their corresponding prototypes. These virtual prototypes can adaptively adjust the margin based on the spatial distribution of embedded features, promoting the proximity of embedded features to their corresponding prototypes and creating clear and discriminative decision boundaries. Furthermore, we introduce a virtual prototype insertion strategy based on hard sample mining, where different synthesis strategies are applied to correctly and incorrectly classified samples, emphasizing the importance of hard samples. SV-Softmax is plug-and-play with minimal computational complexity, without requiring feature or weight normalization nor relying on task-specific hyperparameter tuning. Extensive comparative experiments on multiple visual classification and face recognition datasets demonstrate that SV-Softmax achieves competitive or superior performance compared to nine state-of-the-art methods. The code available at: https://github.com/10zhou/SV-Softmax.