Summary of Fourier or Wavelet Bases As Counterpart Self-attention in Spikformer For Efficient Visual Classification, by Qingyu Wang et al.
Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification
by Qingyu Wang, Duzhen Zhang, Tilelin Zhang, Bo Xu
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 proposes a novel energy-efficient model called Fourier-or-Wavelet-based Spikformer (FWformer) that integrates spiking neural networks and artificial Transformers. The FWformer replaces traditional self-attention mechanisms with spike-form Fourier transform, wavelet transform, or their combinations. This innovative approach allows for both higher accuracy and lower computational cost in visual classification tasks. The authors demonstrate the effectiveness of the FWformer on static image and event-based video datasets, achieving comparable or even higher accuracies, faster running speeds, reduced energy consumption, and lower GPU memory usage compared to traditional spikformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new type of artificial intelligence model called the Fourier-or-Wavelet-based Spikformer. This model is designed to be more efficient and accurate in processing visual information. Instead of using complex self-attention mechanisms, the FWformer uses simpler mathematical transforms like Fourier or wavelet transform. The authors show that this approach can achieve better results than traditional models while also using less energy and memory. |
Keywords
* Artificial intelligence * Classification * Self attention
