What are some popular self-supervised learning methods?

Self-supervised learning (SSL) methods enable models to learn meaningful representations from unlabeled data by creating supervision signals from the data itself. Three widely used approaches include contrastive learning, pretext tasks, and clustering-based methods. These techniques are popular because they reduce reliance on labeled datasets while achieving competitive performance in tasks like image classification, natural language processing, and speech recognition.

Contrastive Learning focuses on training models to distinguish between similar and dissimilar data points. For example, in computer vision, frameworks like SimCLR and MoCo generate augmented versions of the same image (e.g., cropping, color shifts) as positive pairs and treat other images as negatives. The model learns by minimizing the distance between embeddings of positive pairs while maximizing it for negatives. This approach has proven effective in vision tasks, with models like ResNet-50 trained via contrastive learning matching supervised counterparts on ImageNet. Code implementations often use similarity metrics like cosine similarity and losses like NT-Xent (Normalized Temperature-Scaled Cross-Entropy).

Pretext Tasks involve designing auxiliary tasks where labels are derived directly from the data. In natural language processing, BERT uses masked language modeling: random words in a sentence are hidden, and the model predicts them using surrounding context. For images, tasks include predicting image rotation (e.g., RotNet) or reconstructing missing patches (e.g., MAE). These tasks force the model to learn features like spatial relationships or semantic context. Pretext tasks are flexible and domain-agnostic, making them applicable to modalities like video (predicting frame order) or audio (reconstructing spectrograms).

Clustering-Based Methods assign pseudo-labels by grouping data points into clusters and refining representations iteratively. DeepCluster, for instance, applies k-means clustering to image features and uses cluster assignments as classification targets. SwAV (Swapping Assignments between Views) improves efficiency by enforcing consistency between cluster assignments of different augmented views of the same image. These methods avoid costly pairwise comparisons (as in contrastive learning) and scale well to large datasets. Libraries like FAISS are often used to accelerate clustering steps, and recent variants integrate online clustering directly into neural network training loops.

Each method has trade-offs: contrastive learning requires careful augmentation design, pretext tasks depend on task relevance, and clustering methods need efficient assignment algorithms. However, all three provide robust frameworks for learning representations without manual labels, making them foundational tools for developers working with unstructured data.