Real-world data often follows a long-tailed distribution, which makes the performance of
existing classification algorithms degrade heavily. A key issue is that the samples in tail …

Real-world training data usually exhibits long-tailed distribution, where several majority
classes have a significantly larger number of samples than the remaining minority classes …

Real-world data typically follows a long-tailed distribution. When a small sample of tail
classes does not cover the underlying distribution well, methods such as class re-balancing …

Imbalanced datasets with long-tailed distribution widely exist in practice, posing great
challenges for deep networks on how to handle the biased predictions between head …

In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the
difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge …

Real-world tasks are universally associated with training samples that exhibit a long-tailed
class distribution, and traditional deep learning models are not suitable for fitting this …

Deep learning algorithms face great challenges with long-tailed data distribution which,
however, is quite a common case in real-world scenarios. Previous methods tackle the …

Unlike the case when using a balanced training dataset, the per-class recall (ie, accuracy) of
neural networks trained with an imbalanced dataset are known to vary a lot from category to …

The long-tail distribution of the visual world poses great challenges for deep learning based
classification models on how to handle the class imbalance problem. Existing solutions …

Large-scale data from real-world usually follow a long-tailed distribution (ie, a few majority
classes occupy plentiful training data, while most minority classes have few samples) …