Summary of Inflora: Interference-free Low-rank Adaptation For Continual Learning, by Yan-shuo Liang et al.
InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning
by Yan-Shuo Liang, Wu-Jun Li
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method for parameter-efficient fine-tuning (PEFT) in continual learning, called interference-free low-rank adaptation (InfLoRA). Existing PEFT-based continual learning methods excel at adapting to new tasks while maintaining performance on old ones. However, they neglect the crucial aspect of eliminating the interference between tasks. InfLoRA addresses this by injecting learnable parameters that reparameterize pre-trained weights within a subspace, effectively minimizing task interference. This allows for a better balance between stability and plasticity. The proposed method is evaluated on multiple datasets, outperforming state-of-the-art continual learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem called “continual learning”. It’s like trying to learn new things all the time while still remembering what you learned before. The authors created a new way to do this called InfLoRA. They noticed that old tasks can get in the way of learning new ones, so they came up with a solution to fix this problem. By using InfLoRA, the model can remember what it learned before and also learn new things without getting confused. The results show that InfLoRA works better than other methods for continual learning. |
Keywords
» Artificial intelligence » Continual learning » Fine tuning » Low rank adaptation » Parameter efficient
