  Transfer learning is a classic paradigm by which models pretrained on large
"upstream" datasets are adapted to yield good results on "downstream"
specialized datasets. Generally, more accurate models on the "upstream" dataset
tend to provide better transfer accuracy "downstream". In this work, we perform
an in-depth investigation of this phenomenon in the context of convolutional
neural networks (CNNs) trained on the ImageNet dataset, which have been pruned
- that is, compressed by sparsifying their connections. We consider transfer
using unstructured pruned models obtained by applying several state-of-the-art
pruning methods, including magnitude-based, second-order, re-growth,
lottery-ticket, and regularization approaches, in the context of twelve
standard transfer tasks. In a nutshell, our study shows that sparse models can
match or even outperform the transfer performance of dense models, even at high
sparsities, and, while doing so, can lead to significant inference and even
training speedups. At the same time, we observe and analyze significant
differences in the behaviour of different pruning methods.
