# Energy-constrained Self-training for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2101.00316 · HTML: https://mlsanity.com/paper/2101.00316/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 56 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Builds on (most cited first, 30 of 33)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (2019)
- [Generate To Adapt: Aligning Domains using Generative Adversarial Networks](https://mlsanity.com/paper/1704.01705/) (2017)
- [A Theory of Generative ConvNet](https://mlsanity.com/paper/1602.03264/) (2016)
- [Domain Adaptation for Structured Output via Discriminative Patch Representations](https://mlsanity.com/paper/1901.05427/) (2019)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [Adversarial Dropout Regularization](https://mlsanity.com/paper/1711.01575/) (2017)
- [Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach](https://mlsanity.com/paper/1908.09547/) (2019)
- [DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation](https://mlsanity.com/paper/1804.05827/) (2018)
- [Fully Convolutional Adaptation Networks for Semantic Segmentation](https://mlsanity.com/paper/1804.08286/) (2018)
- [Unsupervised Domain Adaptation with Similarity Learning](https://mlsanity.com/paper/1711.08995/) (2017)
- [Label Refinery: Improving ImageNet Classification through Label Progression](https://mlsanity.com/paper/1805.02641/) (2018)
- [Open Set Domain Adaptation for Image and Action Recognition](https://mlsanity.com/paper/1907.12865/) (2019)
- [Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training](https://mlsanity.com/paper/2010.12440/) (2020)
- [Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models](https://mlsanity.com/paper/1911.07421/) (2019)
- [Unsupervised Domain Adaptation via Calibrating Uncertainties](https://mlsanity.com/paper/1907.11202/) (2019)
- [AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation](https://mlsanity.com/paper/2007.06620/) (2020)
- [Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis](https://mlsanity.com/paper/1911.02475/) (2019)
- [Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets](https://mlsanity.com/paper/1907.03030/) (2019)
- [Conservative Wasserstein Training for Pose Estimation](https://mlsanity.com/paper/1911.00962/) (2019)
- [Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition](https://mlsanity.com/paper/1908.01174/) (2019)

## Built on by (most cited first, 16 of 16)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate](https://mlsanity.com/paper/2107.13469/) (2021)
- [Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation](https://mlsanity.com/paper/2106.12497/) (2021)
- [Test-Time Training on Video Streams](https://mlsanity.com/paper/2307.05014/) (2023)
- [Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis](https://mlsanity.com/paper/2106.12499/) (2021)
- [Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation](https://mlsanity.com/paper/2209.07910/) (2022)
- [Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.13467/) (2021)
- [Unsupervised Domain Adaptation for Segmentation with Black-box Source Model](https://mlsanity.com/paper/2208.07769/) (2022)
- [Resolving Domain Shift For Representations Of Speech In Non-Invasive Brain Recordings](https://mlsanity.com/paper/2410.19986/) (2024)
- [Interpreting Depression From Question-wise Long-term Video Recording of SDS Evaluation](https://mlsanity.com/paper/2106.13393/) (2021)
- [Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures](https://mlsanity.com/paper/2201.07106/) (2022)
- [Subtype-Aware Dynamic Unsupervised Domain Adaptation](https://mlsanity.com/paper/2208.07754/) (2022)
- [Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model](https://mlsanity.com/paper/2208.12885/) (2022)
- [Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation](https://mlsanity.com/paper/2305.14589/) (2023)
- [Domain Adaptation Using Pseudo Labels](https://mlsanity.com/paper/2402.06809/) (2024)
- [TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2511.05782/) (2025)

## Abstract (arXiv)

Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain. Recently, the deep self-training involves an iterative process of predicting on the target domain and then taking the confident predictions as hard pseudo-labels for retraining. However, the pseudo-labels are usually unreliable, and easily leading to deviated solutions with propagated errors. In this paper, we resort to the energy-based model and constrain the training of the unlabeled target sample with the energy function minimization objective. It can be applied as a simple additional regularization. In this framework, it is possible to gain the benefits of the energy-based model, while retaining strong discriminative performance following a plug-and-play fashion. We deliver extensive experiments on the most popular and large scale UDA benchmarks of image classification as well as semantic segmentation to demonstrate its generality and effectiveness.