Enhancing Small-Scale Dataset Expansion with Triplet-Connection-based Sample Re-Weighting

TL;DR

This paper introduces TriReWeight, a triplet-connection-based sample re-weighting method that improves dataset expansion quality in computer vision, especially for medical images, by effectively reducing noisy data influence.

Contribution

The paper presents a novel theoretical analysis and a new re-weighting method, TriReWeight, that enhances generative data augmentation without performance degradation.

Findings

Outperforms state-of-the-art methods by 7.9% on natural datasets

Improves medical image dataset augmentation by 3.4%

Theoretically approaches optimal generalization bound

Abstract

The performance of computer vision models in certain real-world applications, such as medical diagnosis, is often limited by the scarcity of available images. Expanding datasets using pre-trained generative models is an effective solution. However, due to the uncontrollable generation process and the ambiguity of natural language, noisy images may be generated. Re-weighting is an effective way to address this issue by assigning low weights to such noisy images. We first theoretically analyze three types of supervision for the generated images. Based on the theoretical analysis, we develop TriReWeight, a triplet-connection-based sample re-weighting method to enhance generative data augmentation. Theoretically, TriReWeight can be integrated with any generative data augmentation methods and never downgrade their performance. Moreover, its generalization approaches the optimal in the order $O(\sqrt{d\ln (n)/n})$. Our experiments validate the correctness of the theoretical analysis and demonstrate that our method outperforms the existing SOTA methods by $7.9\%$ on average over six natural image datasets and by $3.4\%$ on average over three medical datasets. We also experimentally validate that our method can enhance the performance of different generative data augmentation methods.