  Contrastive self-supervised learning has outperformed supervised pretraining
on many downstream tasks like segmentation and object detection. However,
current methods are still primarily applied to curated datasets like ImageNet.
In this paper, we first study how biases in the dataset affect existing
methods. Our results show that current contrastive approaches work surprisingly
well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed
and (iii) general versus domain-specific datasets. Second, given the generality
of the approach, we try to realize further gains with minor modifications. We
show that learning additional invariances -- through the use of multi-scale
cropping, stronger augmentations and nearest neighbors -- improves the
representations. Finally, we observe that MoCo learns spatially structured
representations when trained with a multi-crop strategy. The representations
can be used for semantic segment retrieval and video instance segmentation
without finetuning. Moreover, the results are on par with specialized models.
We hope this work will serve as a useful study for other researchers. The code
and models are available at
https://github.com/wvangansbeke/Revisiting-Contrastive-SSL.
