Summary of Alore: Efficient Visual Adaptation Via Aggregating Low Rank Experts, by Sinan Du et al.
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts
by Sinan Du, Guosheng Zhang, Keyao Wang, Yuanrui Wang, Haixiao Yue, Gang Zhang, Errui Ding, Jingdong Wang, Zhengzhuo Xu, Chun Yuan
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed ALoRE method is a novel parameter-efficient transfer learning (PETL) approach that adapts large-scale vision foundation models to downstream tasks. By reusing the hypercomplex parameterized space constructed using Kronecker products, ALoRE disentangles learned representations and patterns during training. This multi-branch paradigm maintains negligible extra parameters and can be merged into frozen backbones via re-parameterization without increasing inference latency. Experimental results on 24 image classification tasks demonstrate that ALoRE outperforms full fine-tuning strategies and other state-of-the-art PETL methods in terms of performance and parameter efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to adapt large artificial intelligence models for use in specific tasks, like recognizing images. They’ve created a method called ALoRE that helps machines learn from each other’s strengths without needing to retrain everything from scratch. This approach uses special mathematical structures to keep the learned patterns separate and makes it easy to add this knowledge to existing models. The scientists tested ALoRE on 24 different image recognition tasks and found that it outperformed other methods, even when using fewer calculations. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Inference » Parameter efficient » Transfer learning
