Summary of Mtlora: a Low-rank Adaptation Approach For Efficient Multi-task Learning, by Ahmed Agiza et al.
MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning
by Ahmed Agiza, Marina Neseem, Sherief Reda
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called MTLoRA for efficiently adapting pre-trained models to multiple tasks while minimizing the number of parameters updated during fine-tuning. This approach, which combines task-agnostic and task-specific low-rank adaptation modules, enables MTL architectures to handle task specialization and interaction effectively. The authors apply MTLoRA to hierarchical-transformer-based MTL architectures, achieving higher accuracy on downstream dense prediction tasks compared to fully fine-tuning the model while reducing the number of trainable parameters by 3.6x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MTLoRA is a new way to make machines learn multiple things at once without having to change many settings. It’s like a special tool that helps computers adapt what they’ve learned from one task to another task, and it does this while only changing a few small parts of the computer’s memory. This means the computer can learn many things quickly and accurately. The people who made MTLoRA tested it on a big dataset and found that it worked really well, even better than other ways they tried. |
Keywords
» Artificial intelligence » Fine tuning » Low rank adaptation » Transformer
