# Associative Domain Adaptation

arXiv: https://arxiv.org/abs/1708.00938 · HTML: https://mlsanity.com/paper/1708.00938/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 30 of 32)

- [Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration](https://mlsanity.com/paper/2102.12182/) (2021)
- [Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2102.11614/) (2021)
- [Model Adaptation: Unsupervised Domain Adaptation without Source Data](https://mlsanity.com/paper/2502.19316/) (2025)
- [Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach](https://mlsanity.com/paper/2408.07511/) (2024)
- [Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2104.01286/) (2021)
- [CAFA: Class-Aware Feature Alignment for Test-Time Adaptation](https://mlsanity.com/paper/2206.00205/) (2022)
- [Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation](https://mlsanity.com/paper/2103.11661/) (2021)
- [Data-SUITE: Data-centric identification of in-distribution incongruous examples](https://mlsanity.com/paper/2202.08836/) (2022)
- [Inferring Latent Domains for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/2103.13873/) (2021)
- [An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training](https://mlsanity.com/paper/2112.15555/) (2021)
- [Appearance Based Deep Domain Adaptation for the Classification of Aerial Images](https://mlsanity.com/paper/2108.07779/) (2021)
- [From Big to Small: Adaptive Learning to Partial-Set Domains](https://mlsanity.com/paper/2203.07375/) (2022)
- [Dynamic Domain Generalization](https://mlsanity.com/paper/2205.13913/) (2022)
- [Slimmable Domain Adaptation](https://mlsanity.com/paper/2206.06620/) (2022)
- [PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology](https://mlsanity.com/paper/2106.11902/) (2021)
- [Vicinal and categorical domain adaptation](https://mlsanity.com/paper/2103.03460/) (2021)
- [Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers](https://mlsanity.com/paper/2201.11870/) (2022)
- [Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Series](https://mlsanity.com/paper/2503.11217/) (2025)
- [Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2108.00610/) (2021)
- [Action Recognition with Domain Invariant Features of Skeleton Image](https://mlsanity.com/paper/2111.11250/) (2021)
- [Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection](https://mlsanity.com/paper/2209.15439/) (2022)
- [Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields](https://mlsanity.com/paper/2107.06028/) (2021)
- [On the inductive biases of deep domain adaptation](https://mlsanity.com/paper/2109.07920/) (2021)
- [Kernel distance measures for time series, random fields and other structured data](https://mlsanity.com/paper/2109.14752/) (2021)
- [Signature-Graph Networks](https://mlsanity.com/paper/2110.11551/) (2021)
- [GCCN: Global Context Convolutional Network](https://mlsanity.com/paper/2110.11664/) (2021)
- [Cycle Label-Consistent Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2205.13957/) (2022)
- [MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2211.03144/) (2022)
- [SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization](https://mlsanity.com/paper/2312.13377/) (2023)
- [A Batch Normalization Classifier for Domain Adaptation](https://mlsanity.com/paper/2103.11642/) (2021)

## Abstract (arXiv)

We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain invariant embeddings, while minimizing the classification error on the labeled source domain. We accomplish this by reinforcing associations between source and target data directly in embedding space. Our method can easily be added to any existing classification network with no structural and almost no computational overhead. We demonstrate the effectiveness of our approach on various benchmarks and achieve state-of-the-art results across the board with a generic convolutional neural network architecture not specifically tuned to the respective tasks. Finally, we show that the proposed association loss produces embeddings that are more effective for domain adaptation compared to methods employing maximum mean discrepancy as a similarity measure in embedding space.