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。

长尾分类的类实例平衡学习

Class Instance Balanced Learning for Long-Tailed Classification

Learning discriminative image representations plays a vital role in
long-tailed image classification because it can ease the classifier learning in
imbalanced cases. Given the promising performance contrastive learning has
shown recently in representation learning, in this work, we explore effective
supervised contrastive learning strategies and tailor them to learn better
image representations from imbalanced data in order to boost the classification
accuracy thereon. Specifically, we propose a novel hybrid network structure
being composed of a supervised contrastive loss to learn image representations
and a cross-entropy loss to learn classifiers, where the learning is
progressively transited from feature learning to the classifier learning to
embody the idea that better features make better classifiers. We explore two
variants of contrastive loss for feature learning, which vary in the forms but
share a common idea of pulling the samples from the same class together in the
normalized embedding space and pushing the samples from different classes
apart. One of them is the recently proposed supervised contrastive (SC) loss,
which is designed on top of the state-of-the-art unsupervised contrastive loss
by incorporating positive samples from the same class. The other is a
prototypical supervised contrastive (PSC) learning strategy which addresses the
intensive memory consumption in standard SC loss and thus shows more promise
under limited memory budget. Extensive experiments on three long-tailed
classification datasets demonstrate the advantage of the proposed contrastive
learning based hybrid networks in long-tailed classification.

本文研究基于对比学习的监督式分类学习策略，提出了一种混合网络结构，用于从不平衡的数据中学习更好的图像表征，以提高分类精度。具体而言，我们探索了两种对比损失的变体，以推动特征学习，从而实现更好的分类器。实验结果表明，基于对比学习的混合网络在长尾分类中优于传统方法。