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The observation of sparse trainable sub-networks within over-parametrizednetworks - also known as Lottery Tickets (LTs) - has prompted inquiries aroundtheir trainability, scaling, uniqueness, and generalization properties. Across28 combinations of image classification tasks and architectures, we discoverdifferences in the connectivity structure of LTs found through differentiterative pruning techniques, thus disproving their uniqueness and connectingemergent mask structure to the choice of pruning. In addition, we propose aconsensus-based method for generating refined lottery tickets. This lotteryticket denoising procedure, based on the principle that parameters that alwaysgo unpruned across different tasks more reliably identify importantsub-networks, is capable of selecting a meaningful portion of the architecturein an embarrassingly parallel way, while quickly discarding extra parameterswithout the need for further pruning iterations. We successfully train thesesub-networks to performance comparable to that of ordinary lottery tickets.

Bespoke vs. Pr\^et-\`a-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding