A Comprehensive Survey on Self-Supervised Learning on Computer Vision: Moving Beyond the Supervised Paradigm
TL;DR
This survey presents a seven-category taxonomy of self-supervised learning methods covering: 1) input reconstruction or restoration, 2) context prediction, 3) contrastive learning, 4) feature clustering, 5) self-distillation-based feature reconstruction, 6) redundancy reduction, and 7) masked image modeling.
Abstract
The availability of large-scale labeled datasets has driven advances in AI-based computer vision, yet supervised learning remains costly and impractical in domains where annotation is scarce. Self-supervised learning (SSL) addresses this by harnessing unlabeled data to learn rich, transferable representations without explicit supervision. This survey presents a seven-category taxonomy of self-supervised learning methods covering: 1) input reconstruction or restoration, 2) context prediction, 3) contrastive learning, 4) feature clustering, 5) self-distillation-based feature reconstruction, 6) redundancy reduction, and 7) masked image modeling, with coverage extended to recent methods that include DINOv2, I-JEPA, SparK, data2vec 2.0, V-JEPA, DINOv3, V-JEPA 2, V-JEPA 2.1, C-JEPA, PhiNet v2. We situate this work within the existing survey landscape by explicitly comparing our contributions with prior SSL reviews. Beyond method descriptions, we provide: a chronological timeline of SSL evolution from 2008 to 2026; a cross-paradigm comparative analysis evaluating all seven families along collapse risk, scalability, computational cost, and downstream transferability; a dedicated comparative analysis of anti-collapse mechanisms; critical limitations and trade-off analyses per method family; and systematic benchmarking evidence on ImageNet-1K, PASCAL VOC, COCO, and five public medical imaging datasets. We also contribute a practical method selection decision matrix, extended challenge discussions, and actionable open problems for future research.