# Self-Paced Multi-Task Learning

arXiv: https://arxiv.org/abs/1604.01474 · HTML: https://mlsanity.com/paper/1604.01474/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2016.

## Built on by (most cited first, 13 of 13)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Few-Cost Salient Object Detection with Adversarial-Paced Learning](https://mlsanity.com/paper/2104.01928/) (2021)
- [Self-paced Weight Consolidation for Continual Learning](https://mlsanity.com/paper/2307.10845/) (2023)
- [Meta-Learning for Relative Density-Ratio Estimation](https://mlsanity.com/paper/2107.00801/) (2021)
- [Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization](https://mlsanity.com/paper/2306.09222/) (2023)
- [When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review](https://mlsanity.com/paper/2307.14382/) (2023)
- [HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization](https://mlsanity.com/paper/2208.03486/) (2022)
- [Disentangling Transfer and Interference in Multi-Domain Learning](https://mlsanity.com/paper/2107.05445/) (2021)
- [Catheter Detection and Segmentation in X-ray Images via Multi-task Learning](https://mlsanity.com/paper/2503.02717/) (2025)
- [Leveraging convergence behavior to balance conflicting tasks in multi-task learning](https://mlsanity.com/paper/2204.06698/) (2022)
- [Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement](https://mlsanity.com/paper/2402.03557/) (2024)
- [Mixed-order self-paced curriculum learning for universal lesion detection](https://mlsanity.com/paper/2302.04677/) (2023)
- [MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images](https://mlsanity.com/paper/2505.18741/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances equally when training, SPMTL attempts to jointly learn the tasks by taking into consideration the complexities of both tasks and instances. This is inspired by the cognitive process of human brain that often learns from the easy to the hard. We construct a compact SPMTL formulation by proposing a new task-oriented regularizer that can jointly prioritize the tasks and the instances. Thus it can be interpreted as a self-paced learner for MTL. A simple yet effective algorithm is designed for optimizing the proposed objective function. An error bound for a simplified formulation is also analyzed theoretically. Experimental results on toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-art methods.