# One-shot Network Pruning at Initialization with Discriminative Image Patches

Source: [One-shot Network Pruning at Initialization with Discriminative Image Patches](<https://www.sota2.com/research/paper/68fa6d911e209131cea3ab18>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:37:22.464Z

Authors: Yinan Yang, Yu Wang, Ying Ji, Heng Qi, Jien Kato

Published: 2022-09-13T01:41:37.000Z

## Abstract

One-shot Network Pruning at Initialization (OPaI) is an effective method to decrease network pruning costs. Recently, there is a growing belief that data is unnecessary in OPaI. However, we obtain an opposite conclusion by ablation experiments in two representative OPaI methods, SNIP and GraSP. Specifically, we find that informative data is crucial to enhancing pruning performance. In this paper, we propose two novel methods, Discriminative One-shot Network Pruning (DOP) and Super Stitching, to prune the network by high-level visual discriminative image patches. Our contributions are as follows. (1) Extensive experiments reveal that OPaI is data-dependent. (2) Super Stitching performs significantly better than the original OPaI method on benchmark ImageNet, especially in a highly compressed model.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2209.05683>)

[PDF](<https://arxiv.org/pdf/2209.05683.pdf>)