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Guiding Pseudo labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation (CVPR 2023)
This is the official implementation of the CVPR 2023 paper "Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation" - Mattia Litrico, Alessio Del Bue, Pietro Morerio.
https://arxiv.org/abs/2303.03770
@inproceedings{litrico_2023_CVPR,
title={Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation},
author={Litrico, Mattia and Del Bue, Alessio and Morerio, Pietro},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
Abstract
Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in\ multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.
Data Preparation
- Please download the VisDA-C and DomainNet dataset, and put it under
${DATA_ROOT}
. By default${DATA_ROOT}
is set todata
. The prepared directory would look like:
${DATA_ROOT}
├── VISDA-C
│ ├── train
│ ├── validation
│ ├── test
- Please download the DomainNet dataset, and put it under
${DATA_ROOT}
. Notice that we follow MME to use a subset that contains 126 classes from 4 domains, so we also compiled .txt files for your convenience based on the the image labels, provided under ./datasets/domainnet-126/ The prepared directory would look like:
${DATA_ROOT}
├── domainnet-126
│ ├── real
│ ├── sketch
│ ├── clipart
│ ├── painting
│ ├── real_list.txt
│ ├── sketch_list.txt
│ ├── clipart_list.txt
│ ├── painting_list.txt
VisDA-C
Training
VISDA-C experiments are done for train
to validation
adaptation.
Before the test-time adaptation, we should have the source model.
You may train the source model with script train_VISDA-C_source.sh
as shown below.
After obtaining the source models, you will find model weights inside the directory logs
.
Now run train_VISDA-C_target.sh to execute test-time adaptation.
# train source model
bash train_VISDA-C_source.sh
# train TTA
bash train_VISDA-C_target.sh
If you want to change the default ${DATA_ROOT}
, please use the following:
# train source model
bash train_VISDA-C_source.sh <DATA_ROOT>
# train TTA
bash train_VISDA-C_target.sh <DATA_ROOT>
This will reproduce Table. 3 from the main paper:
DomainNet-126
Training
DomainNet-126 experiments are done for 7 domain shifts constructed from combinations of Real
, Sketch
, Clipart
, and Painting
.
Before the test-time adaptation, we should have the source model.
You may train the source model with script train_domainnet-126_source.sh
as shown below.
After obtaining the source models, you will find model weights inside the directory logs
.
Now run train_domainnet-126_target.sh to execute test-time adaptation.
# train source model
# example: bash train_VISDA-C_source.sh real
bash train_domainnet-126_source.sh <SOURCE_DOMAIN>
# train SF-UDA
# example: bash train_VISDA-C_target.sh real sketch
bash train_domainnet-126_target.sh <SOURCE_DOMAIN> <TARGET_DOMAIN>
If you want to change the default ${DATA_ROOT}
, please use the following:
# train source model
# example: bash train_VISDA-C_source.sh real
bash train_domainnet-126_source.sh <SOURCE_DOMAIN> <DATA_ROOT>
# train SF-UDA
# example: bash train_VISDA-C_target.sh real sketch
bash train_domainnet-126_target.sh <SOURCE_DOMAIN> <TARGET_DOMAIN> <DATA_ROOT>
This will reproduce Table. 4 from the main paper:
License
GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright © 2007 Free Software Foundation, Inc. http://fsf.org/