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

• URL: http://arxiv.org/abs/2203.00452v1
• Date: Mon, 28 Feb 2022 01:20:35 GMT
• Title: Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation
• Authors: Zihan Zhang and Xiang Xiang
• Abstract summary: We propose a new method, Gradual Balanced Loss and Adaptive Feature Generator (GLAG) to alleviate imbalance. State-of-the-art results have been achieved on long-tail datasets such as CIFAR100-LT, ImageNetLT, and iNaturalist.
• Score: 19.17617301462919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The real-world data distribution is essentially long-tailed, which poses great challenge to the deep model. In this work, we propose a new method, Gradual Balanced Loss and Adaptive Feature Generator (GLAG) to alleviate imbalance. GLAG first learns a balanced and robust feature model with Gradual Balanced Loss, then fixes the feature model and augments the under-represented tail classes on the feature level with the knowledge from well-represented head classes. And the generated samples are mixed up with real training samples during training epochs. Gradual Balanced Loss is a general loss and it can combine with different decoupled training methods to improve the original performance. State-of-the-art results have been achieved on long-tail datasets such as CIFAR100-LT, ImageNetLT, and iNaturalist, which demonstrates the effectiveness of GLAG for long-tailed visual recognition.

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