Summary of Enhancing Parameter-efficient Fine-tuning Of Vision Transformers Through Frequency-based Adaptation, by Son Thai Ly and Hien V. Nguyen
Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation
by Son Thai Ly, Hien V. Nguyen
First submitted to arxiv on: 28 Nov 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 paper introduces FreqFit, a novel Frequency Fine-tuning module that enhances the adaptability of vision transformer foundation models through parameter-efficient fine-tuning (PEFT) methods. The authors argue that traditional PEFT methods may limit the model’s capacity to capture complex patterns, particularly those associated with high-frequency spectra. To address this issue, FreqFit manipulates features in the frequency domain to allow models to capture subtle patterns more effectively. The approach is simple yet surprisingly effective and can be integrated with all existing PEFT methods to boost their performance. The authors conduct extensive experiments on 24 datasets using both supervised and self-supervised foundational models with various state-of-the-art PEFT methods, revealing that FreqFit consistently improves performance over the original PEFT methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to improve the ability of computer vision models to recognize patterns in images. The authors want to make it easier for these models to learn from small amounts of data without sacrificing their ability to recognize complex patterns. They introduce a new module called FreqFit that helps the model understand high-frequency features, which are important for recognizing subtle image structures. The authors test this approach on 24 different datasets and find that it improves performance by 1-16% compared to existing methods. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Self supervised » Supervised » Vision transformer
