Learning Debiased and Disentangled Representations for Semantic Segmentation
Sanghyeok Chu, Dongwan Kim, Bohyung Han.
This is an official pytorch implementation of "Learning Debiased and Disentangled Representations for Semantic Segmentation", which proposes a model-agnostic and stochastic training scheme for semantic segmentation that facilitates the learning of debiased and disentangled representations.
ClassDrop module and its relevant objective functions are implemented in ./lib/model/seg_hrnet.py
Codes are highly referenced from HRNet(pytorch-v1.1). For train and test the model, please refer to the link.
Citation
If you use this code in a publication, please cite our paper.
@article{chu2021learning,
title={Learning Debiased and Disentangled Representations for Semantic Segmentation},
author={Chu, Sanghyeok and Kim, Dongwan and Han, Bohyung},
journal={Advances in Neural Information Processing Systems},
year={2021}
}
