IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning

TL;DR

This paper introduces IPPRO, a novel importance-based structured pruning method that uses projective space to fairly evaluate filters, reducing performance loss and challenging the size-based pruning bias.

Contribution

It proposes a new importance score in projective space and a pruning strategy that mitigates magnitude bias, improving pruning effectiveness.

Findings

Achieves near-lossless pruning with minimal performance drop.

Outperforms traditional magnitude-based pruning methods.

Expands understanding of importance criteria in neural network pruning.

Abstract

With the growth of demand on neural network compression methods, the structured pruning methods including importance-based approach are actively studied. The magnitude importance and many correlated modern importance criteria often limit the capacity of pruning decision, since the filters with larger magnitudes are not likely to be pruned if the smaller one didn't, even if it is redundant. In this paper, we propose a novel pruning strategy to challenge this dominating effect of magnitude and provide fair chance to each filter to be pruned, by placing it on projective space. After that, we observe the gradient descent movement whether the filters move toward the origin or not, to measure how the filter is likely to be pruned. This measurement is used to construct PROscore, a novel importance score for IPPRO, a novel importance-based structured pruning with magnitude-indifference. Our evaluation results shows that the proposed importance criteria using the projective space achieves near-lossless pruning by reducing the performance drop in pruning, with promising performance after the finetuning. Our work debunks the ``size-matters'' myth in pruning and expands the frontier of importance-based pruning both theoretically and empirically.