  Real-world visual data often exhibits a long-tailed distribution, where some
''head'' classes have a large number of samples, yet only a few samples are
available for ''tail'' classes. Such imbalanced distribution causes a great
challenge for learning a deep neural network, which can be boiled down into a
dilemma: on the one hand, we prefer to increase the exposure of tail class
samples to avoid the excessive dominance of head classes in the classifier
training. On the other hand, oversampling tail classes makes the network prone
to over-fitting, since head class samples are often consequently
under-represented. To resolve this dilemma, in this paper, we propose a
simple-yet-effective auxiliary learning approach. The key idea is to split a
network into a classifier part and a feature extractor part, and then employ
different training strategies for each part. Specifically, to promote the
awareness of tail-classes, a class-balanced sampling scheme is utilised for
training both the classifier and the feature extractor. For the feature
extractor, we also introduce an auxiliary training task, which is to train a
classifier under the regular random sampling scheme. In this way, the feature
extractor is jointly trained from both sampling strategies and thus can take
advantage of all training data and avoid the over-fitting issue. Apart from
this basic auxiliary task, we further explore the benefit of using
self-supervised learning as the auxiliary task. Without using any bells and
whistles, our model achieves superior performance over the state-of-the-art
solutions.
