| Summary: | The success of deep learning techniques in computer vision is largely supported by the availability of large-scale datasets. However, it is difficult to maintain a balanced dataset as the dataset size grows because a few head classes will appear much more frequently compared to a large number of tail classes. Therefore, long-tailed classification is crucial to large scale computer vision tasks. However, many common solutions to long-tailed classification rely on changing the original distribution of the classes in the dataset, causing the information of class structures to be lost. In this paper, we propose a method using a weight-normalization based causal classifier to tackle the long-tailed classification under a causal framework. Specifically, our approach disentangles the magnitude and direction of the weight vectors of the classifier to allow for causal intervention on each of their effects. The model was trained and tested on the Long-tailed CIFAR-10/100 datasets and was able to outperform previous approaches in highly imbalanced datasets.
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