# Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

arXiv: https://arxiv.org/abs/1810.11547 · HTML: https://mlsanity.com/paper/1810.11547/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 30 of 63)

- [FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders](https://mlsanity.com/paper/2103.06413/) (2021)
- [Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2303.03770/) (2023)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Federated Multi-Target Domain Adaptation](https://mlsanity.com/paper/2108.07792/) (2021)
- [Multi-Target Domain Adaptation with Collaborative Consistency Learning](https://mlsanity.com/paper/2106.03418/) (2021)
- [Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation](https://mlsanity.com/paper/2104.00808/) (2021)
- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Semantic Segmentation in Art Paintings](https://mlsanity.com/paper/2203.03238/) (2022)
- [CoNMix for Source-free Single and Multi-target Domain Adaptation](https://mlsanity.com/paper/2211.03876/) (2022)
- [A Differential Entropy Estimator for Training Neural Networks](https://mlsanity.com/paper/2202.06618/) (2022)
- [Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey](https://mlsanity.com/paper/2112.03241/) (2021)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [Domain-Specific Bias Filtering for Single Labeled Domain Generalization](https://mlsanity.com/paper/2110.00726/) (2021)
- [Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2108.06962/) (2021)
- [Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains](https://mlsanity.com/paper/2101.07308/) (2021)
- [Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement](https://mlsanity.com/paper/2201.01929/) (2022)
- [Training-Free Model Merging for Multi-target Domain Adaptation](https://mlsanity.com/paper/2407.13771/) (2024)
- [Adapting Fine-Grained Cross-View Localization to Areas without Fine Ground Truth](https://mlsanity.com/paper/2406.00474/) (2024)
- [Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2204.11667/) (2022)
- [Generalized Domain Adaptation](https://mlsanity.com/paper/2106.01656/) (2021)
- [ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2203.06811/) (2022)
- [Doubly Contrastive Deep Clustering](https://mlsanity.com/paper/2103.05484/) (2021)
- [Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization](https://mlsanity.com/paper/2110.04820/) (2021)
- [Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation](https://mlsanity.com/paper/2311.12682/) (2023)
- [Disentanglement and Generalization Under Correlation Shifts](https://mlsanity.com/paper/2112.14754/) (2021)
- [Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation](https://mlsanity.com/paper/2309.16127/) (2023)
- [Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment](https://mlsanity.com/paper/2107.12216/) (2021)
- [Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer](https://mlsanity.com/paper/2104.06476/) (2021)

## Abstract (arXiv)

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains. In this work we propose an information theoretic approach for domain adaptation in the novel context of multiple target domains with unlabeled instances and one source domain with labeled instances. Our model aims to find a shared latent space common to all domains, while simultaneously accounting for the remaining private, domain-specific factors. Disentanglement of shared and private information is accomplished using a unified information-theoretic approach, which also serves to establish a stronger link between the latent representations and the observed data. The resulting model, accompanied by an efficient optimization algorithm, allows simultaneous adaptation from a single source to multiple target domains. We test our approach on three challenging publicly-available datasets, showing that it outperforms several popular domain adaptation methods.