The long-tailed image classification task remains important in the
development of deep neural networks as it explicitly deals with large
imbalances in the class frequencies of the training data. While uncommon in
engineered datasets, this imbalance is almost always present in real-world
data. Previous approaches have shown that combining cross-entropy and
contrastive learning can improve performance on the long-tailed task, but they
do not explore the tradeoff between head and tail classes. We propose a novel
class instance balanced loss (CIBL), which reweights the relative contributions
of a cross-entropy and a contrastive loss as a function of the frequency of
class instances in the training batch. This balancing favours the contrastive
loss for more common classes, leading to a learned classifier with a more
balanced performance across all class frequencies. Furthermore, increasing the
relative weight on the contrastive head shifts performance from common (head)
to rare (tail) classes, allowing the user to skew the performance towards these
classes if desired. We also show that changing the linear classifier head with
a cosine classifier yields a network that can be trained to similar performance
in substantially fewer epochs. We obtain competitive results on both
CIFAR-100-LT and ImageNet-LT.

该研究提出了一种新的类实例平衡损失 (CIBL) 方法，以在训练数据的类频率不平衡时，通过重新平衡交叉熵损失和对比损失的相对贡献来获得更平衡的性能表现，并且通过使用余弦分类器，可以在更少的 epochs 中获得类似的性能表现。