mlsanityData to 7 Oct 2026

A Unified Perspective on Multi-Domain and Multi-Task Learning

arXiv 1412.7489 ↗First posted 23 Dec 2014stat.ML cs.LG cs.NEcited by 33

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Abstract (arXiv)

In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different ways of constructing semantic descriptors. Our interpretation provides an alternative pipeline for zero-shot learning (ZSL), where a model for a novel class can be constructed without training data. Moreover, it leads to a new and practically relevant problem setting of zero-shot domain adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model for an unseen domain can be generated by its semantic descriptor. Experiments across this range of problems demonstrate that our framework outperforms a variety of alternatives.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2014.

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Citing papers per year: 2021: 12; 2022: 4; 2023: 5; 2024: 5; 2025: 4; 2026: 3.

33 papers.

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  1. 2021One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Predictioncited by 78
  2. 2021Multi-Task Learning in Natural Language Processing: An Overviewcited by 61
  3. 2021Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentationcited by 53
  4. 2021Knowledge-aware Zero-Shot Learning: Survey and Perspectivecited by 41
  5. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  6. 2024Domain Generalization through Meta-Learning: A Surveycited by 28
  7. 2022On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyondcited by 26
  8. 2022Automatic Expert Selection for Multi-Scenario and Multi-Task Searchcited by 9
  9. 2023UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructionscited by 9
  10. 2021Learning to Transfer for Traffic Forecasting via Multi-task Learningcited by 7
  11. 2022A Survey on Visual Transfer Learning using Knowledge Graphscited by 7
  12. 2021CIZSL++: Creativity Inspired Generative Zero-Shot Learningcited by 6
21 more
  1. 2024A separability-based approach to quantifying generalization: which layer is best?cited by 5
  2. 2023Meta Omnium: A Benchmark for General-Purpose Learning-to-Learncited by 4
  3. 2021Provable Adaptation across Multiway Domains via Representation Learningcited by 3
  4. 2024Elastic Multi-Gradient Descent for Parallel Continual Learningcited by 2
  5. 2025Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectivescited by 2
  6. 2021Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learningcited by 1
  7. 2023Decoupled Training: Return of Frustratingly Easy Multi-Domain Learningcited by 1
  8. 2023Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networkscited by 1
  9. 2024Interpretable Multi-task Learning with Shared Variable Embeddingscited by 1
  10. 2025Evaluating Transfer Learning Methods on Real-World Data Streams: A Case Study in Financial Fraud Detectioncited by 1
  11. 2025Federated Learning via Meta-Variational Dropoutcited by 1
  12. 2021Predictive Optimization with Zero-Shot Domain Adaptationcited by 0
  13. 2021A microservice-based framework for exploring data selection in cross-building knowledge transfercited by 0
  14. 2021Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classificationcited by 0
  15. 2021Zero-Shot Learning Based on Knowledge Sharingcited by 0
  16. 2023Robust Representation Learning for Unified Online Top-K Recommendationcited by 0
  17. 2024A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasetscited by 0
  18. 2025Multi-Attribute Multi-Grained Adaptation of Pre-Trained Language Models for Text Understanding from Bayesian Perspectivecited by 0
  19. 2026UME: A Unified Meta-Generalization Framework for Cross-Domain ETAcited by 0
  20. 2026Hierarchical Wasserstein Merging for Multi-Domain Multi-Task Learning: From Specialists to a Generalistcited by 0
  21. 2026Transfer Learning for Evolving Domainscited by 0