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

arXiv: https://arxiv.org/abs/2007.01126 · HTML: https://mlsanity.com/paper/2007.01126/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 18 of 18)

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- [Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey](https://mlsanity.com/paper/2308.08234/) (2023)
- [Pre-trained Language Models as Re-Annotators](https://mlsanity.com/paper/2205.05368/) (2022)
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- [Parameterized Neural Networks for Finance](https://mlsanity.com/paper/2304.08883/) (2023)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research](https://mlsanity.com/paper/2402.19059/) (2024)
- [DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection](https://mlsanity.com/paper/2502.11023/) (2025)
- [Cross-Learning from Scarce Data via Multi-Task Constrained Optimization](https://mlsanity.com/paper/2511.13680/) (2025)

## 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.