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The real-world data distribution is essentially long-tailed, which posesgreat challenge to the deep model. In this work, we propose a new method,Gradual Balanced Loss and Adaptive Feature Generator (GLAG) to alleviateimbalance. GLAG first learns a balanced and robust feature model with GradualBalanced Loss, then fixes the feature model and augments the under-representedtail classes on the feature level with the knowledge from well-represented headclasses. And the generated samples are mixed up with real training samplesduring training epochs. Gradual Balanced Loss is a general loss and it cancombine with different decoupled training methods to improve the originalperformance. State-of-the-art results have been achieved on long-tail datasetssuch as CIFAR100-LT, ImageNetLT, and iNaturalist, which demonstrates theeffectiveness of GLAG for long-tailed visual recognition.

Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation