# Domain Conditioned Adaptation Network

arXiv: https://arxiv.org/abs/2005.06717 · HTML: https://mlsanity.com/paper/2005.06717/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 30 of 43)

- [CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.06165/) (2021)
- [Domain Adaptation via Prompt Learning](https://mlsanity.com/paper/2202.06687/) (2022)
- [Active Learning for Domain Adaptation: An Energy-Based Approach](https://mlsanity.com/paper/2112.01406/) (2021)
- [C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation](https://mlsanity.com/paper/2303.17132/) (2023)
- [Semantic Concentration for Domain Adaptation](https://mlsanity.com/paper/2108.05720/) (2021)
- [ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.10812/) (2021)
- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Generalized Domain Conditioned Adaptation Network](https://mlsanity.com/paper/2103.12339/) (2021)
- [Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2309.12742/) (2023)
- [Gradual Source Domain Expansion for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2311.09599/) (2023)
- [Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation](https://mlsanity.com/paper/2403.18360/) (2024)
- [Unsupervised Domain Adaption Harnessing Vision-Language Pre-training](https://mlsanity.com/paper/2408.02192/) (2024)
- [Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.02089/) (2021)
- [Domain Adaptation via Maximizing Surrogate Mutual Information](https://mlsanity.com/paper/2110.12184/) (2021)
- [Enhancing Domain Adaptation through Prompt Gradient Alignment](https://mlsanity.com/paper/2406.09353/) (2024)
- [Dynamic Domain Adaptation for Efficient Inference](https://mlsanity.com/paper/2103.16403/) (2021)
- [NoisyNN: Exploring the Impact of Information Entropy Change in Learning Systems](https://mlsanity.com/paper/2309.10625/) (2023)
- [Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2411.07794/) (2024)
- [Towards Corruption-Agnostic Robust Domain Adaptation](https://mlsanity.com/paper/2104.10376/) (2021)
- [Enhanced Separable Disentanglement for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.11915/) (2021)
- [Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning](https://mlsanity.com/paper/2210.09486/) (2022)
- [Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation](https://mlsanity.com/paper/2310.08071/) (2023)
- [On Evolving Attention Towards Domain Adaptation](https://mlsanity.com/paper/2103.13561/) (2021)
- [Deep Least Squares Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02207/) (2021)
- [Reciprocal Normalization for Domain Adaptation](https://mlsanity.com/paper/2112.10474/) (2021)
- [Transforming Future Data Center Operations and Management via Physical AI](https://mlsanity.com/paper/2504.04982/) (2025)
- [Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.00727/) (2021)
- [MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel Weighting](https://mlsanity.com/paper/2110.01777/) (2021)
- [Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection](https://mlsanity.com/paper/2303.14317/) (2023)
- [CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2304.09623/) (2023)

## Abstract (arXiv)

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.