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There have been long-standing controversies and inconsistencies over theexperiment setup and criteria for identifying the "winning ticket" inliterature. To reconcile such, we revisit the definition of lottery tickethypothesis, with comprehensive and more rigorous conditions. Under our newdefinition, we show concrete evidence to clarify whether the winning ticketexists across the major DNN architectures and/or applications. Throughextensive experiments, we perform quantitative analysis on the correlationsbetween winning tickets and various experimental factors, and empirically studythe patterns of our observations. We find that the key traininghyperparameters, such as learning rate and training epochs, as well as thearchitecture characteristics such as capacities and residual connections, areall highly correlated with whether and when the winning tickets can beidentified. Based on our analysis, we summarize a guideline for parametersettings in regards of specific architecture characteristics, which we hope tocatalyze the research progress on the topic of lottery ticket hypothesis. Ourcodes are publicly available at:https://github.com/boone891214/sanity-check-LTH.

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?