Meta Feature Modulator for Long-tailed Recognition

Understand

Deep neural networks often degrade significantly when training data suffer from class imbalance problems.

• Existing approaches, e.g., re-sampling and re-weighting, commonly address this issue by rearranging the label distribution of training data to train the networks fitting well to the implicit balanced label distribution.
• However, most of them hinder the representative ability of learned features due to insufficient use of intra/inter-sample information of training data.
• To address this issue, we propose meta feature modulator (MFM), a meta-learning framework to model the difference between the long-tailed training data and the balanced meta data from the perspective of representation learning.