mlsanityData to 7 Oct 2026

A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning

arXiv 2007.01126 ↗First posted 2 Jul 2020cs.LG cs.CV stat.MLcited by 18

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Abstract (arXiv)

Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each task. Auxiliary tasks can be added to the main task to ultimately boost the performance. In this paper, we provide a brief review on the recent deep multi-task learning (dMTL) approaches followed by methods on selecting useful auxiliary tasks that can be used in dMTL to improve the performance of the model for the main task.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 8; 2024: 2; 2025: 3.

18 papers.

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  1. 2021Medical Image Segmentation with 3D Convolutional Neural Networks: A Surveycited by 16
  2. 2023MultiModN- Multimodal, Multi-Task, Interpretable Modular Networkscited by 14
  3. 2023Inductive biases of multi-task learning and finetuning: multiple regimes of feature reusecited by 9
  4. 2022Generative multitask learning mitigates target-causing confoundingcited by 4
  5. 2025PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completioncited by 4
  6. 2023MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculturecited by 3
  7. 2024Guided Quantum Compression for High Dimensional Data Classificationcited by 3
  8. 2023Meta-Auxiliary Learning for Adaptive Human Pose Predictioncited by 2
  9. 2021Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flowcited by 1
  10. 2022Multi-task Bias-Variance Trade-off Through Functional Constraintscited by 1
  11. 2023Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Surveycited by 1
  12. 2022Pre-trained Language Models as Re-Annotatorscited by 0
6 more
  1. 2023Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimationscited by 0
  2. 2023Parameterized Neural Networks for Financecited by 0
  3. 2023Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023cited by 0
  4. 2024WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Researchcited by 0
  5. 2025DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detectioncited by 0
  6. 2025Cross-Learning from Scarce Data via Multi-Task Constrained Optimizationcited by 0