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Diffusion Mechanism in Neural Network: Theory and Applications

This repository contains the code for Diff-ResNet implemented with PyTorch.

More details in paper: Diffusion Mechanism in Residual Neural Network: Theory and Applications

Introduction

Inspired by the diffusive ODEs, we propose a novel diffusion residual network (Diff-ResNet) to strengthen the interactions among data points. Diffusion mechanism can decrease the distance-diameter ratio and improves the separability of data points. Figure below shows the evolution of points with diffusion.

The figure describes the architecture of our network.

Synthetic Data

We offer several toy examples to test the effect of diffusion mechanism and for users to understand how to use diffusion in a plug-and-play manner.

They can serve as minimal working examples of diffusion mechanism. Simply run each python file.

Graph Learning

Code is adapted from Pitfalls of graph neural network evaluation. Users can test our Diff-ResNet on dataset cora, citeseer and pubmed for 100 random dataset splits and 20 random initializations each. One should provide step_size and layer_num. Specific parameter choice for reproducing results in paper is provided in the appendix.

python train.py --dataset cora --step_size 0.25 --layer_num 20 --dropout 0.25

Few-shot

1. Dataset

Download miniImageNet, tieredImageNet and CUB-100. Unpack these dataset in to corresponding dataset name directory in data/.

2. Backbone Training

You can download pretrained models on base classes here, and unpack pretrained models in fewshot/saved_models/.

Or you can train from scratch by running train_backbone.py.

python train_backbone.py --dataset mini --backbone resnet18 --silent --epochs 100

3. Diff-ResNets Classification

Run train.py with specified arguments for few-shot classification. Specific parameter choice for reproducing results in paper is provided in the appendix. See argument description for help.

python train.py --dataset mini --backbone resnet18 --shot 1 --method diffusion --step_size 0.5 --layer_num 6

Citation

If you find Diff-ResNets useful in your research, please consider citing:

@article{wang2024diffusion,
  author={Wang, Tangjun and Dou, Zehao and Bao, Chenglong and Shi, Zuoqiang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Diffusion Mechanism in Residual Neural Network: Theory and Applications}, 
  year={2024},
  volume={46},
  number={2},
  pages={667-680},
  doi={10.1109/TPAMI.2023.3272341}
}