> ## Documentation Index > Fetch the complete documentation index at: https://aegean.ai/llms.txt > Use this file to discover all available pages before exploring further. # Self-Supervised Learning > Self-supervised representation learning for anomaly detection ## Self-supervised Learned (SSL) Representations In what we will be calling from now on *Self-Supervised Learning* (SSL), we implicitly provide a supervisory signal to the representation learning process to improve performance by pulling the similar images closer together and pushing the dissimilar ones further apart. The modeling approach follows this pipeline: ``` Pretrained Featurizer → SSL → Anomaly Detection ↓ ↓ Initial Features Final Features ``` ## Introduction to Contrastive Learning The idea around contrastive learning is to pull together an anchor and a "positive" sample in embedding space, and push apart the anchor from many "negative" samples. Since no labels are available, a positive pair often consists of data augmentations of the sample, and negative pairs are formed by the anchor and randomly chosen samples from the mini-batch. Contrastive Learning with ResNet ## Supervised Contrastive Learning Supervised contrastive learning extends the contrastive learning paradigm by incorporating label information when available. This allows the model to leverage both the self-supervised contrastive signal and the supervised classification signal. Supervised Cross-Entropy *** [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/products/applications/anomaly-detection/manufacturing/unsupervised-learning-models/self-supervised/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).