Pruning Randomly Initialized Neural Networks with Iterative Randomization

Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro INOUE

Keywords: lottery ticket hypothesis, approximation theorem, neural network pruning

Abstract: Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis. Ramanujan et al. (2020) empirically showed that only pruning the weights can achieve remarkable performance instead of optimizing the weight values. However, to achieve the same level of performance as the weight optimization, the pruning approach requires more parameters in the networks before pruning and thus more memory space. To overcome this parameter inefficiency, we introduce a novel framework to prune randomly initialized neural networks with iteratively randomizing weight values (IteRand). Theoretically, we prove an approximation theorem in our framework, which indicates that the randomizing operations are provably effective to reduce the required number of the parameters. We also empirically demonstrate the parameter efficiency in multiple experiments on CIFAR-10 and ImageNet.

Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.

TL;DR: We propose a novel framework of iterative randomization (IteRand) for pruning randomly initialized neural networks, and demonstrate the parameter efficiency both theoretically and empirically.

Code: https://github.com/dchiji-ntt/iterand

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/pruning-randomly-initialized-neural-networks/code)