Contrastive learning and self-supervised learning (SSL) are complementary techniques that enable models to learn meaningful representations from unlabeled data. Contrastive learning is a specific training strategy that teaches models to differentiate between similar and dissimilar data points, while SSL is a broader paradigm that creates supervisory signals directly from the data itself. Together, they form a powerful framework for training models without relying on manual labels.
In practice, SSL defines the task that generates these signals, often through data transformations or structural assumptions. For example, in computer vision, a common SSL approach is to apply random crops, rotations, or color distortions to an image and train the model to recognize that these altered versions originate from the same source. Contrastive learning then operationalizes this by comparing pairs of data points: similar pairs (augmented views of the same image) are pulled closer in the model’s representation space, while dissimilar pairs (different images) are pushed apart. This is typically implemented using a contrastive loss function like NT-Xent, which measures similarity using cosine distance and applies a temperature scaling parameter to control the sharpness of distinctions. By combining SSL’s task design with contrastive learning’s optimization mechanics, the model learns to capture high-level features useful for downstream tasks like classification or object detection.
A concrete example is SimCLR, a widely used framework in computer vision. It uses SSL to generate positive pairs by applying two random augmentations to the same image and treats all other images in the batch as negatives. The model then uses a contrastive loss to maximize agreement between the positive pairs while minimizing similarity to negatives. Similarly, in NLP, models like SimCSE leverage dropout noise—applying different dropout masks to the same sentence—to create positive pairs for contrastive learning. These examples highlight how SSL defines the “rules” for creating meaningful data relationships, while contrastive learning provides the mechanism to enforce those relationships during training. The result is a model that generalizes well even when labeled data is scarce.