# Multi-Domain Adversarial Learning

arXiv: https://arxiv.org/abs/1903.09239 · HTML: https://mlsanity.com/paper/1903.09239/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 19 of 19)

- [Generalized Multimodal ELBO](https://mlsanity.com/paper/2105.02470/) (2021)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve](https://mlsanity.com/paper/2102.03924/) (2021)
- [Free Lunch for Domain Adversarial Training: Environment Label Smoothing](https://mlsanity.com/paper/2302.00194/) (2023)
- [On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond](https://mlsanity.com/paper/2203.09513/) (2022)
- [Multi-source Few-shot Domain Adaptation](https://mlsanity.com/paper/2109.12391/) (2021)
- [Multi-Scenario Ranking with Adaptive Feature Learning](https://mlsanity.com/paper/2306.16732/) (2023)
- [Unsupervised Non-transferable Text Classification](https://mlsanity.com/paper/2210.12651/) (2022)
- [Augmentation based unsupervised domain adaptation](https://mlsanity.com/paper/2202.11486/) (2022)
- [Estimating the treatment effect over time under general interference through deep learner integrated TMLE](https://mlsanity.com/paper/2412.04799/) (2024)
- [Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights](https://mlsanity.com/paper/2207.07255/) (2022)
- [Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation](https://mlsanity.com/paper/2302.05990/) (2023)
- [Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning](https://mlsanity.com/paper/2102.13147/) (2021)
- [Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning](https://mlsanity.com/paper/2309.10302/) (2023)
- [DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction](https://mlsanity.com/paper/2208.01373/) (2022)
- [Regularizing Neural Network Training via Identity-wise Discriminative Feature Suppression](https://mlsanity.com/paper/2209.14553/) (2022)
- [Multi-domain learning CNN model for microscopy image classification](https://mlsanity.com/paper/2304.10616/) (2023)
- [Transfer Learning for T-Cell Response Prediction](https://mlsanity.com/paper/2403.12117/) (2024)
- [FAST-Q: Fast-track Exploration with Adversarially Balanced State Representations for Counterfactual Action Estimation in Offline Reinforcement Learning](https://mlsanity.com/paper/2504.21383/) (2025)

## Abstract (arXiv)

Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.