Class Instance Balanced Learning for Long-Tailed Classification
Marc-Antoine Lavoie Steven Waslander
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.