Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning. (arXiv:2206.08347v1 [cs.CV])

By leveraging contrastive learning, clustering, and other pretext tasks,
unsupervised methods for learning image representations have reached impressive
results on standard benchmarks. The result has been a crowded field - many
methods with substantially different implementations yield results that seem
nearly identical on popular benchmarks, such as linear evaluation on ImageNet.
However, a single result does not tell the whole story. In this paper, we
compare methods using performance-based benchmarks such as linear evaluation,
nearest neighbor classification, and clustering for …

analysis arxiv benchmarking cv image learning representation representation learning unsupervised