Summary of Petah: Parameter Efficient Task Adaptation For Hybrid Transformers in a Resource-limited Context, by Maximilian Augustin et al.
PETAH: Parameter Efficient Task Adaptation for Hybrid Transformers in a resource-limited Context
by Maximilian Augustin, Syed Shakib Sarwar, Mostafa Elhoushi, Sai Qian Zhang, Yuecheng Li, Barbara De Salvo
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Transformers have gained popularity in computer vision following their success in natural language processing (NLP). However, due to high compute requirements, many resource-constrained applications still rely on convolutional or hybrid models. Hybrid transformers combine the benefits of convolution and attention layers but struggle with task adaptation, a technique that allows for a shared transformer backbone for multiple downstream tasks. This work introduces PETAH: Parameter Efficient Task Adaptation for Hybrid Transformers, which achieves excellent performance while reducing storage requirements at negligible cost. By combining PETAH with pruning, we demonstrate highly performant and storage-friendly models for multi-tasking on classification and other vision tasks, outperforming established task-adaptation techniques for ViTs while being more efficient on mobile hardware. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks into how to make computer vision work better with transformers. Transformers are popular because they’re good at processing language, but they use a lot of computing power. Other models, like convolutional or hybrid models, are still used because they’re more suitable for resource-constrained applications. The challenge is making these hybrid transformers work well for multiple tasks at once. This study introduces PETAH, a way to adapt hybrid transformers for different tasks while saving storage space and keeping performance high. By combining PETAH with pruning, the researchers show that their approach outperforms other methods and is more efficient on mobile devices. |
Keywords
» Artificial intelligence » Attention » Classification » Natural language processing » Nlp » Parameter efficient » Pruning » Transformer
