Min-Kook Suh, Seung-Woo Seo
Although contrastive learning methods have shown prevailing performance on a variety of representation learning tasks, they encounter difficulty when the training dataset is long-tailed. Many researchers have combined contrastive learning and a logit adjustment technique to address this problem, but the combinations are done ad-hoc and a theoretical background has not yet been provided. The goal of this paper is to provide the background and further improve the performance. First, we show that the fundamental reason contrastive learning methods struggle with long-tailed tasks is that they try to maximize the mutual information maximization between latent features and input data. As ground-truth labels are not considered in the maximization, they are not able to address imbalances between class labels. Rather, we interpret the long-tailed recognition task as a mutual information maximization between latent features and ground-truth labels. This approach integrates contrastive learning and logit adjustment seamlessly to derive a loss function that shows state-of-the-art performance on long-tailed recognition benchmarks. It also demonstrates its efficacy in image segmentation tasks, verifying its versatility beyond image classification.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Image Classification | CIFAR-100-LT (ρ=50) | Error Rate | 41.9 | GML (ResNet-32) |
| Image Classification | CIFAR-100-LT (ρ=10) | Error Rate | 33 | GML (ResNet-32) |
| Image Classification | ImageNet-LT | Top-1 Accuracy | 58.8 | GML (ResNeXt-50) |
| Image Classification | CIFAR-100-LT (ρ=100) | Error Rate | 46 | GML (ResNet-32) |
| Few-Shot Image Classification | CIFAR-100-LT (ρ=50) | Error Rate | 41.9 | GML (ResNet-32) |
| Few-Shot Image Classification | CIFAR-100-LT (ρ=10) | Error Rate | 33 | GML (ResNet-32) |
| Few-Shot Image Classification | ImageNet-LT | Top-1 Accuracy | 58.8 | GML (ResNeXt-50) |
| Few-Shot Image Classification | CIFAR-100-LT (ρ=100) | Error Rate | 46 | GML (ResNet-32) |
| Generalized Few-Shot Classification | CIFAR-100-LT (ρ=50) | Error Rate | 41.9 | GML (ResNet-32) |
| Generalized Few-Shot Classification | CIFAR-100-LT (ρ=10) | Error Rate | 33 | GML (ResNet-32) |
| Generalized Few-Shot Classification | ImageNet-LT | Top-1 Accuracy | 58.8 | GML (ResNeXt-50) |
| Generalized Few-Shot Classification | CIFAR-100-LT (ρ=100) | Error Rate | 46 | GML (ResNet-32) |
| Long-tail Learning | CIFAR-100-LT (ρ=50) | Error Rate | 41.9 | GML (ResNet-32) |
| Long-tail Learning | CIFAR-100-LT (ρ=10) | Error Rate | 33 | GML (ResNet-32) |
| Long-tail Learning | ImageNet-LT | Top-1 Accuracy | 58.8 | GML (ResNeXt-50) |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | Error Rate | 46 | GML (ResNet-32) |
| Generalized Few-Shot Learning | CIFAR-100-LT (ρ=50) | Error Rate | 41.9 | GML (ResNet-32) |
| Generalized Few-Shot Learning | CIFAR-100-LT (ρ=10) | Error Rate | 33 | GML (ResNet-32) |
| Generalized Few-Shot Learning | ImageNet-LT | Top-1 Accuracy | 58.8 | GML (ResNeXt-50) |
| Generalized Few-Shot Learning | CIFAR-100-LT (ρ=100) | Error Rate | 46 | GML (ResNet-32) |