The recent “Lottery ticket hypothesis” by Frankle and Garbin [4] demonstrated a way to find trainable subnets of neural networks that achieve same or better accuracy as the original unpruned network. These networks, dubbed winning tickets, are identified by training a neural net, pruning smallest-magnitude weights and resetting the remaining weights to their original initializations. We examine if these tickets are trainable only because it has seen the same training data in the previous pruning iteration. As the process of uncovering a ticket is slow and tedious, we explore a faster alternative by using a fraction of the dataset for pruning iterations and examine its performance when retrained with the entire dataset. We observe that a speed-up of 7.5x can be achieved by using subset (10%) of training data to generate winning tickets while achieving the same accuracy when retrained on the full dataset. We also discover a winning ticket for Shufflenet, a network architecture with 48 layers, that makes use of depthwise separable convolutions.

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