exemplar learning

Exemplar learning is a self-supervised learning approach that trains models to distinguish between different transformed versions of the same image treating each image as its own class. The model learns that augmented views of an image like crops rotations and color jittering should have similar representations while different images should be distinct. This creates a pretext task requiring the model to learn useful visual features without labels. The approach uses a memory bank or momentum encoder to store representations of all training images. Loss functions like NCE or InfoNCE maximize similarity between augmented views of the same image while minimizing similarity to other images. Exemplar learning was foundational for modern contrastive methods like SimCLR MoCo and BYOL. It works because distinguishing between thousands of image instances requires learning semantic features about objects textures and scenes. Pretrained models transfer well to downstream tasks like classification detection and segmentation often matching supervised pretraining performance.