# Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

arXiv: https://arxiv.org/abs/1710.03850 · HTML: https://mlsanity.com/paper/1710.03850/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2017.

## Built on by (most cited first, 19 of 19)

- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition](https://mlsanity.com/paper/2207.07730/) (2022)
- [ConTinTin: Continual Learning from Task Instructions](https://mlsanity.com/paper/2203.08512/) (2022)
- [Towards Lifelong Learning of End-to-end ASR](https://mlsanity.com/paper/2104.01616/) (2021)
- [Cognitively Inspired Learning of Incremental Drifting Concepts](https://mlsanity.com/paper/2110.04662/) (2021)
- [A Theory for Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2208.06931/) (2022)
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- [Continual Task Allocation in Meta-Policy Network via Sparse Prompting](https://mlsanity.com/paper/2305.18444/) (2023)
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- [Improved Region Proposal Network for Enhanced Few-Shot Object Detection](https://mlsanity.com/paper/2308.07535/) (2023)
- [Lifetime policy reuse and the importance of task capacity](https://mlsanity.com/paper/2106.01741/) (2021)
- [Preserving Fairness in AI under Domain Shift](https://mlsanity.com/paper/2301.12369/) (2023)
- [Mastering Continual Reinforcement Learning through Fine-Grained Sparse Network Allocation and Dormant Neuron Exploration](https://mlsanity.com/paper/2503.05246/) (2025)
- [Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution](https://mlsanity.com/paper/2308.02714/) (2023)
- [Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network](https://mlsanity.com/paper/2502.15662/) (2025)
- [Hyperparameter Analysis for Derivative Compressive Sampling](https://mlsanity.com/paper/2108.04355/) (2021)
- [Robust Internal Representations for Domain Generalization](https://mlsanity.com/paper/2309.15522/) (2023)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)

## Abstract (arXiv)

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model the inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.