Summary of Dct-based Decorrelated Attention For Vision Transformers, by Hongyi Pan et al.
DCT-Based Decorrelated Attention for Vision Transformers
by Hongyi Pan, Emadeldeen Hamdan, Xin Zhu, Koushik Biswas, Ahmet Enis Cetin, Ulas Bagci
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 two methods to improve the performance of Vision Transformers (ViT) by enhancing their self-attention mechanism. The first method introduces a simple yet innovative initialization approach using Discrete Cosine Transform (DCT) coefficients, which significantly improves accuracy in classification tasks. This is achieved by initializing attention weights with DCT-based values, providing a robust foundation for the attention mechanism. The second method proposes a novel compression technique based on DCT, reducing the size of weight matrices while maintaining accuracy. By truncating high-frequency DCT components of input patches, the proposed compression reduces computational overhead without compromising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes Vision Transformers better by improving how they focus on important parts of images. It does this in two ways: first, it gives them a good starting point to learn from, and second, it helps them use less memory and compute power. The result is more accurate predictions with less effort! |
Keywords
» Artificial intelligence » Attention » Classification » Self attention » Vit
