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Existing long-tailed classification (LT) methods only focus on tackling theclass-wise imbalance that head classes have more samples than tail classes, butoverlook the attribute-wise imbalance. In fact, even if the class is balanced,samples within each class may still be long-tailed due to the varyingattributes. Note that the latter is fundamentally more ubiquitous andchallenging than the former because attributes are not just implicit for mostdatasets, but also combinatorially complex, thus prohibitively expensive to bebalanced. Therefore, we introduce a novel research problem: GeneralizedLong-Tailed classification (GLT), to jointly consider both kinds of imbalances.By "generalized", we mean that a GLT method should naturally solve thetraditional LT, but not vice versa. Not surprisingly, we find that mostclass-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLTand MSCOCO-GLT. We argue that it is because they over-emphasize the adjustmentof class distribution while neglecting to learn attribute-invariant features.To this end, we propose an Invariant Feature Learning (IFL) method as the firststrong baseline for GLT. IFL first discovers environments with divergentintra-class distributions from the imperfect predictions and then learnsinvariant features across them. Promisingly, as an improved feature backbone,IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, andensemble. Codes and benchmarks are available on Github:https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorch

Invariant Feature Learning for Generalized Long-Tailed Classification