Few-Shot Learning with Global Class Representations
This addresses the problem of few-shot learning for AI systems by enabling joint training on base and novel classes, though it is incremental as it builds on existing meta-learning pipelines.
The paper tackles few-shot learning by learning global class representations using both base and novel class samples from the start, with a sample synthesis strategy to prevent overfitting, achieving effective results in both standard and generalized few-shot learning settings.
In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples. In each training episode, an episodic class mean computed from a support set is registered with the global representation via a registration module. This produces a registered global class representation for computing the classification loss using a query set. Though following a similar episodic training pipeline as existing meta learning based approaches, our method differs significantly in that novel class training samples are involved in the training from the beginning. To compensate for the lack of novel class training samples, an effective sample synthesis strategy is developed to avoid overfitting. Importantly, by joint base-novel class training, our approach can be easily extended to a more practical yet challenging FSL setting, i.e., generalized FSL, where the label space of test data is extended to both base and novel classes. Extensive experiments show that our approach is effective for both of the two FSL settings.