Conventional de-noising methods rely on the assumption that all samples are independent and identically distributed, so the resultant classifier, though disturbed by noise, can still easily identify the noises as the outliers of training distribution. However, the assumption is unrealistic in large-scale data that is inevitably long-tailed. Such imbalanced training data makes a classifier less discriminative for the tail classes, whose previously "easy" noises are now turned into "hard" ones -- they are almost as outliers as the clean tail samples. We introduce this new challenge as Noisy Long-Tailed Classification (NLT). Not surprisingly, we find that most de-noising methods fail to identify the hard noises, resulting in significant performance drop on the three proposed NLT benchmarks: ImageNet-NLT, Animal10-NLT, and Food101-NLT. To this end, we design an iterative noisy learning framework called Hard-to-Easy (H2E). Our bootstrapping philosophy is to first learn a classifier as noise identifier invariant to the class and context distributional changes, reducing "hard" noises to "easy" ones, whose removal further improves the invariance. Experimental results show that our H2E outperforms state-of-the-art de-noising methods and their ablations on long-tailed settings while maintaining a stable performance on the conventional balanced settings. Datasets and codes are available at https://github.com/yxymessi/H2E-Framework

翻译：传统去噪方法依赖于所有样本独立同分布的假设，因此即便受到噪声干扰，所得分类器仍能轻易将噪声识别为训练分布中的离群点。然而，在大规模长尾数据中该假设并不成立。这种不平衡的训练数据会降低分类器对尾部类别的判别能力，使得原本"容易"的噪声转变为"困难"噪声——它们几乎与干净尾部样本一样成为离群点。我们将这一新挑战称为噪声长尾分类（NLT）。不出所料，我们发现大多数去噪方法无法识别困难噪声，导致在提出的三个NLT基准测试集（ImageNet-NLT、Animal10-NLT和Food101-NLT）上性能显著下降。为此，我们设计了一种名为"从难到易"（H2E）的迭代噪声学习框架。其自举哲学在于：首先训练一个对类别和上下文分布变化具有不变性的分类器作为噪声识别器，将"困难"噪声降级为"容易"噪声，继而通过移除噪声进一步改善这种不变性。实验结果表明，我们的H2E方法在长尾设置下优于当前最先进的去噪方法及其消融实验，同时在传统平衡设置下保持稳定性能。数据集与代码已开源于https://github.com/yxymessi/H2E-Framework