论文标题
长尾分类，逐渐平衡损失和自适应特征产生
Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation
论文作者
论文摘要
实际数据分布基本上是长尾巴，这对深层模型构成了巨大挑战。在这项工作中，我们提出了一种新方法，逐渐平衡的损失和自适应功能生成器（GLAG），以减轻失衡。 Glag首先学习了具有逐渐平衡损失的平衡和健壮的功能模型，然后修复功能模型，并在功能级别上增强代表性不足的尾部类别，并具有代表性良好的头等舱的知识。在训练时期，生成的样品与实际训练样品混合在一起。逐渐平衡的损失是一般损失，它可以与不同的解耦训练方法结合在一起，以提高原始性能。在诸如CIFAR100-LT，ImagEnetlt和Inaturalist之类的长尾数据集上已经实现了最新的结果，这些结果证明了Glag对长尾视觉识别的有效性。
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.
