Feb. 13, 2024, 5:44 a.m. | Alexander Theus Olin Geimer Friedrich Wicke Thomas Hofmann Sotiris Anagnostidis Sidak Pal Singh

cs.LG updates on arXiv.org arxiv.org

Structural pruning of neural networks conventionally relies on identifying and discarding less important neurons, a practice often resulting in significant accuracy loss that necessitates subsequent fine-tuning efforts. This paper introduces a novel approach named Intra-Fusion, challenging this prevailing pruning paradigm. Unlike existing methods that focus on designing meaningful neuron importance metrics, Intra-Fusion redefines the overlying pruning procedure. Through utilizing the concepts of model fusion and Optimal Transport, we leverage an agnostically given importance metric to arrive at a more effective …

accuracy cs.cv cs.lg designing fine-tuning focus fusion importance loss meta metrics networks neural networks neuron neurons novel paper paradigm practice pruning transport via

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