Deep clustering is a technique within self-supervised learning (SSL) that uses clustering objectives to train neural networks without labeled data. It aligns with SSL’s core idea of creating supervisory signals directly from unlabeled data. In deep clustering, the model learns to group similar data points by optimizing a clustering loss (e.g., grouping images of cats vs. dogs) while simultaneously refining its feature representations. This dual process—improving clusters and features iteratively—makes it a natural fit for SSL frameworks, where the goal is to learn useful representations for downstream tasks like classification or detection.
A key example is the DeepCluster method, which alternates between clustering image embeddings (using algorithms like k-means) and updating the neural network to predict cluster assignments. For instance, in computer vision, a model might group unlabeled images into clusters based on visual patterns, then train on pseudo-labels derived from those clusters. Another example is SwAV (Swapping Assignments between Views), which clusters augmented views of the same image and enforces consistency between their cluster assignments. These approaches eliminate the need for manual labels by treating cluster identities as temporary targets. Similarly, in NLP, clustering word or sentence embeddings could help group semantically similar phrases, which the model then uses as training signals. By iterating between clustering and representation learning, the model discovers structure in the data that benefits tasks like sentiment analysis or machine translation.
Deep clustering’s main advantage is its ability to scale to large datasets without labels, making it practical for domains like medical imaging or audio processing where annotations are costly. However, challenges include computational overhead from frequent clustering steps and the risk of degenerate solutions (e.g., all points collapsing into one cluster). To address this, methods often combine clustering with other SSL techniques like contrastive learning. For example, SCAN (Semantic Clustering by Adopting Nearest neighbors) first uses contrastive learning to pretrain features, then applies clustering to refine them. This hybrid approach balances the stability of contrastive methods with the structure-discovery benefits of clustering. For developers, implementing deep clustering requires efficient clustering algorithms (e.g., mini-batch k-means) and careful hyperparameter tuning to avoid instability, but it offers a flexible way to leverage unlabeled data for representation learning.