Instance Discrimination is the foundational contrastive learning paradigm where each image in the dataset is treated as its own unique class — and the model is trained to distinguish each instance from all others, learning representations that capture fine-grained visual differences.

What Is Instance Discrimination?

• Definition: Treat the N images in the dataset as N classes.
• Positive: Augmented versions of the same image.
• Negative: All other images.
• Loss: NCE/InfoNCE applied to the N-class discrimination task.
• Paper: Wu et al., "Unsupervised Feature Learning via Non-Parametric Instance Discrimination" (2018).

Why It Matters

• Foundation: SimCLR, MoCo, BYOL, and DINO are all built on the instance discrimination framework.
• No Labels Needed: The "class" of each image is its identity — no human annotation required.
• Semantic Emergence: Despite training with instance-level labels, learned features capture semantic similarity (a surprising and powerful property).

Instance Discrimination is the philosophical foundation of contrastive SSL — the insight that treating every image as unique can paradoxically teach a model to understand what makes images similar.