Home

Awesome

Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

by Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng.

Introduction

This repository is for our ECCV2020 paper 'Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization'. cellgraph The framework of the proposed EISNet for domain generalization. We train a feature Encoder $f$ for discriminative and transferable feature extraction and a classifier for object recognition. Two complementary tasks, a momentum metric learning task and a self-supervised auxiliary task, are introduced to prompt general feature learning. We maintain a momentum updated Encoder (MuEncoder) to generate momentum updated embeddings stored in a large memory bank. Also, we design a $K$-hard negative selector to locate the informative hard triplets from the memory bank to calculate the triplet loss. The auxiliary self-supervised task predicts the order of patches within an image.

Requirements

conda create -n EISNet python=3.6.8

Usage

  1. Clone the repository and download the dataset PACS and VLCS into folder EISNet/Dataset.

If you have already put the dataset into other paths, you need to change path in txt files in EISNet/code/data/txt_lists/*.txt.

  1. Train the model.

    sh run_PACS_photo_Resnet50.sh
    

Citation

If EISNet is useful for your research, please consider citing:

@inproceedings{wang2020learning,
  title={Learning from Extrinsic and IntrinsicSupervisions for Domain Generalization},
  author={Wang, Shujun and Yu, Lequan and Li, Caizi and Fu, Chi-Wing and Heng, Pheng-Ann},
  booktitle={ECCV}, 
  year={2020}
}