# Zero-Shot Deep Domain Adaptation

arXiv: https://arxiv.org/abs/1707.01922 · HTML: https://mlsanity.com/paper/1707.01922/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2017.

## Built on by (most cited first, 30 of 35)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains](https://mlsanity.com/paper/2102.12206/) (2021)
- [Zero-Shot Day-Night Domain Adaptation with a Physics Prior](https://mlsanity.com/paper/2108.05137/) (2021)
- [EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition](https://mlsanity.com/paper/2403.14082/) (2024)
- [Cross-Domain Video Anomaly Detection without Target Domain Adaptation](https://mlsanity.com/paper/2212.07010/) (2022)
- [Deep vanishing point detection: Geometric priors make dataset variations vanish](https://mlsanity.com/paper/2203.08586/) (2022)
- [Cross-Modal Knowledge Transfer Without Task-Relevant Source Data](https://mlsanity.com/paper/2209.04027/) (2022)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [Adversarial Imitation Learning with Trajectorial Augmentation and Correction](https://mlsanity.com/paper/2103.13887/) (2021)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [Similarity Min-Max: Zero-Shot Day-Night Domain Adaptation](https://mlsanity.com/paper/2307.08779/) (2023)
- [Deep Learning for Scene Classification: A Survey](https://mlsanity.com/paper/2101.10531/) (2021)
- [Failure Modes of Domain Generalization Algorithms](https://mlsanity.com/paper/2111.13733/) (2021)
- [Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data](https://mlsanity.com/paper/2305.12011/) (2023)
- [Learning Disentangled Prompts for Compositional Image Synthesis](https://mlsanity.com/paper/2306.00763/) (2023)
- [Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence](https://mlsanity.com/paper/2407.18899/) (2024)
- [Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models](https://mlsanity.com/paper/2404.12920/) (2024)
- [Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2206.03149/) (2022)
- [ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer](https://mlsanity.com/paper/2403.13652/) (2024)
- [Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation](https://mlsanity.com/paper/2405.05336/) (2024)
- [UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement](https://mlsanity.com/paper/2507.00721/) (2025)
- [Zero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptation](https://mlsanity.com/paper/2508.03300/) (2025)
- [Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations](https://mlsanity.com/paper/2101.06848/) (2021)
- [Multinational Address Parsing: A Zero-Shot Evaluation](https://mlsanity.com/paper/2112.04008/) (2021)
- [Learning Good Features to Transfer Across Tasks and Domains](https://mlsanity.com/paper/2301.11310/) (2023)
- [WildFit: Autonomous In-situ Model Adaptation for Resource-Constrained IoT Systems](https://mlsanity.com/paper/2409.07796/) (2024)
- [Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition](https://mlsanity.com/paper/2109.13420/) (2021)
- [Domain Adaptation via Rebalanced Sub-domain Alignment](https://mlsanity.com/paper/2302.02009/) (2023)
- [Task Guided Compositional Representation Learning for ZDA](https://mlsanity.com/paper/2109.05934/) (2021)

## Abstract (arXiv)

Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.