How Well Do Self-Supervised Models Transfer?
This work provides a large-scale comparative evaluation for researchers and practitioners in computer vision to understand the transferability of self-supervised models.
This paper evaluates 13 self-supervised models on 40 downstream tasks, including recognition, object detection, and dense prediction. It finds that the best self-supervised models generally outperform supervised baselines, confirming recent trends.
Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction. We compare their performance to a supervised baseline and show that on most tasks the best self-supervised models outperform supervision, confirming the recently observed trend in the literature. We find ImageNet Top-1 accuracy to be highly correlated with transfer to many-shot recognition, but increasingly less so for few-shot, object detection and dense prediction. No single self-supervised method dominates overall, suggesting that universal pre-training is still unsolved. Our analysis of features suggests that top self-supervised learners fail to preserve colour information as well as supervised alternatives, but tend to induce better classifier calibration, and less attentive overfitting than supervised learners.