Data to 14 Oct 2026

Domain Agnostic Learning with Disentangled Representations

arXiv 1904.12347 ↗First posted 28 Apr 2019cs.CV cs.LGcited by 108

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Abstract (arXiv)

Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of target data into distinct domains is known as a priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL): How to transfer knowledge from a labeled source domain to unlabeled data from arbitrary target domains? To tackle this problem, we devise a novel Deep Adversarial Disentangled Autoencoder (DADA) capable of disentangling domain-specific features from class identity. We demonstrate experimentally that when the target domain labels are unknown, DADA leads to state-of-the-art performance on several image classification datasets.

Key facts. Cited by 108 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Apr 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 31; 2022: 27; 2023: 26; 2024: 13; 2025: 8; 2026: 3.

108 papers.

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2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
2021Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representationscited by 96
2021Invariant Information Bottleneck for Domain Generalizationcited by 86
2021Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentationcited by 81
2022Full-Spectrum Out-of-Distribution Detectioncited by 63
2022ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalizationcited by 54
2021Vector-Decomposed Disentanglement for Domain-Invariant Object Detectioncited by 47
2022BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptationcited by 39
2021Curriculum Graph Co-Teaching for Multi-Target Domain Adaptationcited by 37
2021Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generationcited by 35
2022Domain-invariant Feature Exploration for Domain Generalizationcited by 34

38 more

2021ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptationcited by 33
2021Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimizationcited by 32
2021A Survey of Unsupervised Domain Adaptation for Visual Recognitioncited by 31
2022Out-of-Distribution Representation Learning for Time Series Classificationcited by 31
2021An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wildcited by 30
2022BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learningcited by 29
2022Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferringcited by 28
2021Where and What? Examining Interpretable Disentangled Representationscited by 27
2021Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentationcited by 26
2022Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classificationcited by 25
2022Learning Invariant Visual Representations for Compositional Zero-Shot Learningcited by 24
2023DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalizationcited by 24
2021Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learningcited by 23
2022CoNMix for Source-free Single and Multi-target Domain Adaptationcited by 23
2024HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shiftscited by 23
2022Domain Generalization via Frequency-domain-based Feature Disentanglement and Interactioncited by 21
2021Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretrainingcited by 19
2021Variational Disentanglement for Domain Generalizationcited by 18
2022AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentationcited by 18
2023MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalizationcited by 18
2021Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentationcited by 16
2022Learnware: Small Models Do Bigcited by 16
2021Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domainscited by 15
2022Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglementcited by 15
2022A Contrastive Objective for Learning Disentangled Representationscited by 14
2022Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarizationcited by 14
2023Domain Generalisation via Risk Distribution Matchingcited by 14
2022Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentationcited by 12
2021Generalized Domain Adaptationcited by 10
2022ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentationcited by 10
2023Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressionscited by 10
2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
2025Gradient-Guided Annealing for Domain Generalizationcited by 9
2021Disentanglement and Generalization Under Correlation Shiftscited by 8
2022HMOE: Hypernetwork-based Mixture of Experts for Domain Generalizationcited by 8
2023Generalizing Across Domains in Diabetic Retinopathy via Variational Autoencoderscited by 8
2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
2021Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfercited by 6

50 most cited of 108 All 108 (JSON)