  The computer vision world has been re-gaining enthusiasm in various
pre-trained models, including both classical ImageNet supervised pre-training
and recently emerged self-supervised pre-training such as simCLR and MoCo.
Pre-trained weights often boost a wide range of downstream tasks including
classification, detection, and segmentation. Latest studies suggest that
pre-training benefits from gigantic model capacity. We are hereby curious and
ask: after pre-training, does a pre-trained model indeed have to stay large for
its downstream transferability?
  In this paper, we examine supervised and self-supervised pre-trained models
through the lens of the lottery ticket hypothesis (LTH). LTH identifies highly
sparse matching subnetworks that can be trained in isolation from (nearly)
scratch yet still reach the full models' performance. We extend the scope of
LTH and question whether matching subnetworks still exist in pre-trained
computer vision models, that enjoy the same downstream transfer performance.
Our extensive experiments convey an overall positive message: from all
pre-trained weights obtained by ImageNet classification, simCLR, and MoCo, we
are consistently able to locate such matching subnetworks at 59.04% to 96.48%
sparsity that transfer universally to multiple downstream tasks, whose
performance see no degradation compared to using full pre-trained weights.
Further analyses reveal that subnetworks found from different pre-training tend
to yield diverse mask structures and perturbation sensitivities. We conclude
that the core LTH observations remain generally relevant in the pre-training
paradigm of computer vision, but more delicate discussions are needed in some
cases. Codes and pre-trained models will be made available at:
https://github.com/VITA-Group/CV_LTH_Pre-training.

Machine Learning;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing