# Deep Asymmetric Multi-task Feature Learning

arXiv: https://arxiv.org/abs/1708.00260 · HTML: https://mlsanity.com/paper/1708.00260/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 15 of 15)

- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://mlsanity.com/paper/2109.04617/) (2021)
- [Auto-Lambda: Disentangling Dynamic Task Relationships](https://mlsanity.com/paper/2202.03091/) (2022)
- [Independent Component Alignment for Multi-Task Learning](https://mlsanity.com/paper/2305.19000/) (2023)
- [KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2205.06784/) (2022)
- [Curriculum-based Asymmetric Multi-task Reinforcement Learning](https://mlsanity.com/paper/2211.03352/) (2022)
- [Exceeding the Limits of Visual-Linguistic Multi-Task Learning](https://mlsanity.com/paper/2107.13054/) (2021)
- [STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map](https://mlsanity.com/paper/2307.03374/) (2023)
- [Towards Principled Task Grouping for Multi-Task Learning](https://mlsanity.com/paper/2402.15328/) (2024)
- [AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics](https://mlsanity.com/paper/2508.13979/) (2025)
- [Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey](https://mlsanity.com/paper/2308.08234/) (2023)
- [Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors](https://mlsanity.com/paper/2112.03032/) (2021)
- [Towards an Efficient ML System: Unveiling a Trade-off between Task Accuracy and Engineering Efficiency in a Large-scale Car Sharing Platform](https://mlsanity.com/paper/2210.06585/) (2022)
- [Parameterized Neural Networks for Finance](https://mlsanity.com/paper/2304.08883/) (2023)
- [Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries](https://mlsanity.com/paper/2410.15875/) (2024)
- [TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection](https://mlsanity.com/paper/2608.29577/) (2026)

## Abstract (arXiv)

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors for the easy tasks to have high contribution to the feature learning while suppressing the influences of unreliable predictors for more difficult tasks. This allows the learning of less noisy representations, and enables unreliable predictors to exploit knowledge from the reliable predictors via the shared latent features. Such asymmetric knowledge transfer through shared features is also more scalable and efficient than inter-task asymmetric transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for multitask learning and image classification, on which it significantly outperforms existing symmetric and asymmetric multitask learning models, by effectively preventing negative transfer in deep feature learning.