Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win
Jaron Maene; Mingxiao Li; Marie-Francine Moens · 2021 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
The finding, in one line
“However, the subsequent work has failed to replicate this on large-scale models and required rewinding to an early stable state instead of initialization.”
Abstract (excerpt)
The lottery ticket hypothesis states that sparse subnetworks exist in randomly initialized dense networks that can be trained to the same accuracy as the dense network they reside in. However, the subsequent work has failed to replicate this on large-scale models and required rewinding to an early stable state instead of initialization. We show that by using a training method that is stable with respect to linear mode connectivity, large networks can also be entirely rewound to initialization. Our subsequent experiments on common vision tasks give strong credence to the hypothesis in Evci et a
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Metadata source: arXiv