PushPull Convolutions

Accepted at ICPR 2024

Quick Links

• ICPR Supplementary Material
• arXiv pre-print

Paper Summary

PushPull-Conv: Improving the robustness of traditional convolutional neural networks (ConvNets) against typical image corruptions, by a simple architectural change (biologically inspired). Replace the first Conv layer with the PushPull-Conv unit to achieve robustness. The goal is to achieve robustness to certain unseen distributions during model learning.

Dataset

Datasets used: CIFAR-10, ImageNet-100, ImageNet-200, and ImageNet-1k. Models were trained on the clean images, and tested on corrupted versions (CIFAR10-C and ImageNet-C; hendrycks et. al). An example of the 15 types of corruption with 5 levels of severity is shown below:

Experiments & Results

Results on ImageNet are presented below. For other datasets and models please refer to the paper. In the Table below, E is the top1 clean error, mCE is mean corruption error and R_net is net reduction in error rate when compared to the baseline. R_net is expressed as a percentage, and accounts for both E and mCE (Refer to Sec. 5.2 in the paper for further details).

Variants of baseline models	E ↓	mCE↓	R_net↑
ResNet50 (baseline)	0.269	0.667	0.00
ResNet50 + PushPull avg3 (PP3)	0.282	0.645	1.67
ResNet50 + PushPull avg5 (PP5)	0.276	0.645	2.79
ResNet50 + AutoAug	0.269	0.630	6.86
ResNet50 + AutoAug + PP3	0.283	0.604	9.05
ResNet50 + AugMix	0.269	0.612	10.14
ResNet50 + AugMix + PP3	0.287	0.590	10.97
ResNet50 + PRIME	0.298	0.522	21.57
ResNet50 + PRIME + PP3	0.306	0.500	24.31

Instructions to run the code

• The current implementation is suited to run on HPC Clusters, an example script is located here.
• A simplified script is being developed to quickly run both the training and the predict flows.

Citation

If you find this useful in your work, please consider citing:

@misc{bennabhaktula2024push, 
    title={{PushPull-Net: Inhibition-driven ResNet} robust to image corruptions},   
    author={Guru Swaroop Bennabhaktula and Enrique Alegre and Nicola Strisciuglio and George Azzopardi},  
    year={2024},  
    eprint={2408.04077},  
    archivePrefix={arXiv},  
    primaryClass={cs.CV},  
    url={https://arxiv.org/abs/2408.04077},   
}