StableNet

StableNet is a deep stable learning method for out-of-distribution generalization.

This is the official repo for CVPR21 paper "Deep Stable Learning for Out-Of-Distribution Generalization" and the arXiv version can be found at https://arxiv.org/abs/2104.07876.

Please note that some hyper-parameters(such as lrbl, epochb, lambdap) may affect the performance , which can vary among different tasks/environments/software/hardware/random seeds, and thus careful tunning is required. Similar to other DG repositories, direct migration may lead to different results as ours. We are sorry for this and trying to address this problem in the following work.

Introduction

Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. Conventional methods assume either the known heterogeneity of training data (e.g. domain labels) or the approximately equal capacities of different domains. In this paper, we consider a more challenging case where neither of the above assumptions holds. We propose to address this problem by removing the dependencies between features via learning weights for training samples, which helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between discriminative features and labels. Extensive experiments clearly demonstrate the effectiveness of our method on multiple distribution generalization benchmarks compared with state-of-the-art counterparts. Through extensive experiments on distribution generalization benchmarks including PACS, VLCS, MNIST-M, and NICO, we show the effectiveness of our method compared with state-of-the-art counterparts.

Installation

Requirements

Linux with Python >= 3.6
PyTorch >= 1.1.0
torchvision >= 0.3.0
tensorboard >= 1.14.0

Quick Start

Train StableNet

python main_stablenet.py --gpu 0

You can see more options from

python main_stablenet.py -h

Result files will be saved in results/.

Performance and trained models

setting	dataset	source domain	target domain	network	dataset split	accuracy	trained model
unbalanced(5:1:1)	PACS	A,C,S	photo	ResNet18	split file	94.864	model file
unbalanced(5:1:1)	PACS	C,S,P	art_painting	ResNet18	split file	80.344	model file
unbalanced(5:1:1)	PACS	A,S,P	cartoon	ResNet18	split file	74.249	model file
unbalanced(5:1:1)	PACS	A,C,P	sketch	ResNet18	split file	71.046	model file
unbalanced(5:1:1)	VLCS	L,P,S	caltech	ResNet18	split file	88.776	model file
unbalanced(5:1:1)	VLCS	C,P,S	labelme	ResNet18	split file	63.243	model file
unbalanced(5:1:1)	VLCS	C,L,S	pascal	ResNet18	split file	66.383	model file
unbalanced(5:1:1)	VLCS	C,L,P	sun	ResNet18	split file	55.459	model file
flexible(5:1:1)	PACS	-	-	ResNet18	split file	45.964	model file
flexible(5:1:1)	VLCS	-	-	ResNet18	split file	81.157	model file

Citing StableNet

If you find this repo useful for your research, please consider citing the paper.

@inproceedings{zhang2021deep,
  title={Deep Stable Learning for Out-Of-Distribution Generalization},
  author={Zhang, Xingxuan and Cui, Peng and Xu, Renzhe and Zhou, Linjun and He, Yue and Shen, Zheyan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5372--5382},
  year={2021}
}

xxgege / StableNet

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