Feedback-guided Data Synthesis for Imbalanced Classification

Understand

Current status quo in machine learning is to use static datasets of real images for training, which often come from long-tailed distributions.

• With the recent advances in generative models, researchers have started augmenting these static datasets with synthetic data, reporting moderate performance improvements on classification tasks.
• We hypothesize that these performance gains are limited by the lack of feedback from the classifier to the generative model, which would promote the usefulness of the generated samples to improve the classifier's performance.
• In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model.