Summary of Adaptive Layer Selection For Efficient Vision Transformer Fine-tuning, by Alessio Devoto et al.
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning
by Alessio Devoto, Federico Alvetreti, Jary Pomponi, Paolo Di Lorenzo, Pasquale Minervini, Simone Scardapane
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 an efficient fine-tuning method for Vision Transformers (ViTs) called ALaST (Adaptive Layer Selection Fine-Tuning for Vision Transformers). The method aims to speed up the fine-tuning process while reducing computational cost, memory load, and training time. ALaST adaptsively estimates the importance of all layers and assigns “compute budgets” accordingly, allocating lower budgets to less critical layers or freezing/floating them to reduce resources consumption. This approach enables a nearly-optimal schedule for distributing computational resources across layers, resulting in substantial reductions in training time (up to 1.5x), FLOPs (up to 2x), and memory load (up to 2x) compared to traditional full fine-tuning approaches. ALaST can be successfully combined with other parameter-efficient fine-tuning methods, such as LoRA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a faster way to make Vision Transformers better for certain tasks. Currently, making these models work for edge or low-energy applications is hard because it takes too much computer power and memory. The new method, called ALaST, makes the process more efficient by only using the parts of the model that are really important. This means less computer power and memory are needed, which makes it faster and more useful. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Parameter efficient
