Extending Contrastive Learning to the Supervised Setting

In recent years, self-supervised representation learning, which is used in a variety of image and video tasks, has significantly advanced due to the application of contrastive learning. These contrastive learning approaches typically teach a model to pull together the representations of a target image (a.k.a., the “anchor”) and a matching (“positive”) image in embedding space, while also pushing apart the anchor from many non-matching (“negative”) images. Because labels are assumed to be unavailable in self-supervised learning, the positive is often an augmentation of the anchor, and the negatives are chosen to be the other samples from the training minibatch. However, because of this random sampling, false negatives, i.e., negatives generated from samples of the same class as the anchor, can cause a degradation in the representation quality. Furthermore, determining the optimal method to generate positives is still an area of active research.

In contrast to the self-supervised approach, a fully-supervised approach could use labeled data to generate positives from existing same-class examples, providing more variability in pretraining than could typically be achieved by simply augmenting the anchor. However, very little work has been done to successfully apply contrastive learning in the fully-supervised domain.

In “Supervised Contrastive Learning”, presented at NeurIPS 2020, we propose a novel loss function, called SupCon, that bridges the gap between self-supervised learning and fully supervised learning and enables contrastive learning to be applied in the supervised setting. Leveraging labeled data, SupCon encourages normalized embeddings from the same class to be pulled closer together, while embeddings from different classes are pushed apart. This simplifies the process of positive selection, while avoiding potential false negatives. Because it accommodates multiple positives per anchor, this approach results in an improved selection of positive examples that are more varied, while still containing semantically relevant information. SupCon also allows label information to play an active role in representation learning rather than restricting it to be used only in downstream training, as is the case for conventional contrastive learning. To the best of our knowledge, this is the first contrastive loss to consistently perform better on large-scale image classification problems than the common approach of using cross-entropy loss to train the model directly. Importantly, SupCon is straightforward to implement and stable to train, provides consistent improvement to top-1 accuracy for a number of datasets and architectures (including Transformer architectures), and is robust to image corruptions and hyperparameter variations.

Self-supervised (left) vs supervised (right) contrastive losses: The self-supervised contrastive loss contrasts a single positive for each anchor (i.e., an augmented version of the same image) against a set of negatives consisting of the entire remainder of the minibatch. The supervised contrastive loss considered in this paper, however, contrasts the set of all samples from the same class as positives against the negatives from the remainder of the batch.

The Supervised Contrastive Learning Framework
SupCon can be seen as a generalization of both the SimCLR and N-pair losses — the former uses positives generated from the same sample as that of the anchor, and the latter uses positives generated from different samples by exploiting known class labels. The use of many positives and many negatives for each anchor allows SupCon to achieve state-of-the-art performance without the need for hard negative mining (i.e., searching for negatives similar to the anchor), which can be difficult to tune properly.

SupCon subsumes multiple losses from the literature and is a generalization of the SimCLR and N-Pair losses.

This method is structurally similar to those used in self-supervised contrastive learning, with modifications for supervised classification. Given an input batch of data, we first apply data augmentation twice to obtain two copies, or “views,” of each sample in the batch (though one could create and use any number of augmented views). Both copies are forward propagated through an encoder network, and the resulting embedding is then L2-normalized. Following standard practice, the representation is further propagated through an optional projection network to help identify meaningful features. The supervised contrastive loss is computed on the normalized outputs of the projection network. Positives for an anchor consist of the representations originating from the same batch instance as the anchor or from other instances with the same label as the anchor; the negatives are then all remaining instances. To measure performance on downstream tasks, we train a linear classifier on top of the frozen representations.

Cross-entropy, self-supervised contrastive loss and supervised contrastive loss Left: The cross-entropy loss uses labels and a softmax loss to train a classifier. Middle: The self-supervised contrastive loss uses a contrastive loss and data augmentations to learn representations. Right: The supervised contrastive loss also learns representations using a contrastive loss, but uses label information to sample positives in addition to augmentations of the same image.

Key Findings
SupCon consistently boosts top-1 accuracy compared to cross-entropy, margin classifiers (with use of labels), and self-supervised contrastive learning techniques on CIFAR-10 and CIFAR-100 and ImageNet datasets. With SupCon, we achieve excellent top-1 accuracy on the ImageNet dataset with the ResNet-50 and ResNet-200 architectures. On ResNet-200, we achieve a top-1 accuracy of 81.4%, which is a 0.8% improvement over the state-of-the-art cross-entropy loss using the same architecture (which represents a significant advance for ImageNet). We also compared cross-entropy and SupCon on a Transformer-based ViT-B/16 model and found a consistent improvement over cross-entropy (77.8% versus 76% for ImageNet; 92.6% versus 91.6% for CIFAR-10) under the same data augmentation regime (without any higher-resolution fine-tuning).

The SupCon loss consistently outperforms cross-entropy with standard data augmentation strategies (AutoAugment, RandAugment and CutMix). We show top-1 accuracy for ImageNet, on ResNet-50, ResNet-101 and ResNet200.

We also demonstrate analytically that the gradient of our loss function encourages learning from hard positives and hard negatives. The gradient contributions from hard positives/negatives are large while those for easy positives/negatives are small. This implicit property allows the contrastive loss to sidestep the need for explicit hard mining, which is a delicate but critical part of many losses, such as triplet loss. See the supplementary material of our paper for a full derivation.

SupCon is also more robust to natural corruptions, such as noise, blur and JPEG compression. The mean Corruption Error (mCE) measures the average degradation in performance compared to the benchmark ImageNet-C dataset. The SupCon models have lower mCE values across different corruptions compared to cross-entropy models, showing increased robustness.

We show empirically that the SupCon loss is less sensitive than cross-entropy to a range of hyperparameters. Across changes in augmentations, optimizers, and learning rates, we observe significantly lower variance in the output of the contrastive loss. Moreover, applying different batch sizes while holding all other hyperparameters constant results in consistently better top-1 accuracy of SupCon to that of cross-entropy at each batch size.

Accuracy of cross-entropy and supervised contrastive loss as a function of hyperparameters and training data size, measured on ImageNet with a ResNet-50 encoder. Left: Boxplot showing Top-1 accuracy vs changes in augmentation, optimizer and learning rates. SupCon yields more consistent results across variations in each, which is useful when the best strategies are unknown a priori. Right: Top-1 accuracy as a function of batch size shows both losses benefit from larger batch sizes while SupCon has higher Top-1 accuracy, even when trained with small batch sizes.
Accuracy of supervised contrastive loss as a function of training duration and the temperature hyperparameter, measured on ImageNet with a ResNet-50 encoder. Left: Top-1 accuracy as a function of SupCon pre-training epochs. Right: Top-1 accuracy as a function of temperature during the pre-training stage for SupCon. Temperature is an important hyperparameter in contrastive learning and reducing sensitivity to temperature is desirable.

Broader Impact and Next Steps
This work provides a technical advancement in the field of supervised classification. Supervised contrastive learning can improve both the accuracy and robustness of classifiers with minimal complexity. The classic cross-entropy loss can be seen as a special case of SupCon where the views correspond to the images and the learned embeddings in the final linear layer corresponding to the labels. We note that SupCon benefits from large batch sizes, and being able to train the models on smaller batches is an important topic for future research.

Our Github repository includes Tensorflow code to train the models in the paper. Our pre-trained models are also released on TF-Hub.

The NeurIPS paper was jointly co-authored with Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Special thanks to Jenny Huang for leading the writing process for this blogpost.

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