[ICLR 2023] DFPC: Data flow driven pruning of coupled channels without data.

This repository is for the new deep neural network pruning method introduced in the following ICLR 2023 paper:

DFPC: Data flow driven pruning of coupled channels without data. [Camera Ready]
Tanay Narshana, Chaitanya Murti, and Chiranjib Bhattacharyya
Indian Institute of Science, Bengaluru, India.

TLDR: This paper introduces a novel method for pruning of networks containing coupled connections without using data, DFPC.

We posit "coupled connections" as a bottleneck to obtain lower inference latencies when pruning networks. We then provide a formalization to abstract "coupled connections" and use it to derive a data-free way to measure the importance of coupled neurons in a network. Our experimental results display the merit in pruning "coupled connections" for they obtain pruned models with a better latency-vs-accuracy.

We're working up to clean up our code and provide our models in a clean way. Everything should be up by mid June Coming up..

1. Data-free code for pruning mobilenets
2. Data-driven code for pruning resnets
3. pruned models for resnet-50 in the data-driven regime.

Available Code:

1. Code for data-free experiments is available in the Data-Free folder.
2. Pruned models for the data-driven experiment for ResNet-50 on the ImageNet dataset is available in the Pruned-Models folder.

Feel free to contact us at tanay.narshana@gmail.com. (Email is more recommended if you'd like quicker reply)

Reference

Please cite this in your publication if our work helps your research:

@inproceedings{narshana2023dfpc,
title={{DFPC}: Data flow driven pruning of coupled channels without data.},
author={Tanay Narshana and Chaitanya Murti and Chiranjib Bhattacharyya},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=mhnHqRqcjYU}
}

Acknowledgments

We are grateful for the code made available by pytorch imagenet example, Regularization-Pruning, and pytorch-summary.