Provably Improving Generalization of Few-Shot Models with Synthetic Data
This addresses the challenge of improving generalization in few-shot learning for computer vision applications, representing a novel method for a known bottleneck rather than a foundational breakthrough.
The paper tackles the problem of performance degradation in few-shot image classification when using synthetic data due to distribution gaps, by developing a theoretical framework and algorithm that integrates prototype learning to bridge this gap. The result is superior performance compared to state-of-the-art methods across multiple datasets.
Few-shot image classification remains challenging due to the scarcity of labeled training examples. Augmenting them with synthetic data has emerged as a promising way to alleviate this issue, but models trained on synthetic samples often face performance degradation due to the inherent gap between real and synthetic distributions. To address this limitation, we develop a theoretical framework that quantifies the impact of such distribution discrepancies on supervised learning, specifically in the context of image classification. More importantly, our framework suggests practical ways to generate good synthetic samples and to train a predictor with high generalization ability. Building upon this framework, we propose a novel theoretical-based algorithm that integrates prototype learning to optimize both data partitioning and model training, effectively bridging the gap between real few-shot data and synthetic data. Extensive experiments results show that our approach demonstrates superior performance compared to state-of-the-art methods, outperforming them across multiple datasets.