# Self-Supervised Domain Adaptation with Consistency Training

arXiv: https://arxiv.org/abs/2010.07539 · HTML: https://mlsanity.com/paper/2010.07539/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders](https://mlsanity.com/paper/2206.09900/) (2022)
- [Transferrable Contrastive Learning for Visual Domain Adaptation](https://mlsanity.com/paper/2112.07516/) (2021)
- [Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction](https://mlsanity.com/paper/2107.09372/) (2021)
- [Adversarial and Random Transformations for Robust Domain Adaptation and Generalization](https://mlsanity.com/paper/2211.06788/) (2022)
- [RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks](https://mlsanity.com/paper/2104.08689/) (2021)
- [GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder](https://mlsanity.com/paper/2410.08023/) (2024)

## Abstract (arXiv)

We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation (specifically, image rotation) and ask the learner to predict the properties of the transformation. However, the obtained feature representation may contain a large amount of irrelevant information with respect to the main task. To provide further guidance, we force the feature representation of the augmented data to be consistent with that of the original data. Intuitively, the consistency introduces additional constraints to representation learning, therefore, the learned representation is more likely to focus on the right information about the main task. Our experimental results validate the proposed method and demonstrate state-of-the-art performance on classical domain adaptation benchmarks. Code is available at https://github.com/Jiaolong/ss-da-consistency.