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Fourier and Wavelet Bases in Spikformer for Efficient Visual Classification


Core Concepts
Replacing SSA with Fourier and Wavelet bases in Spikformer improves efficiency and accuracy in visual classification.
Abstract
The paper introduces the FWformer, replacing SSA with Fourier and Wavelet bases in Spikformer for visual classification tasks. It discusses the energy-efficient spikformer, the limitations of SSA, and the proposed hypothesis. The FWformer achieves higher accuracy, reduced computational cost, and improved efficiency compared to the standard spikformer. The paper also explores the orthogonality of self-attention and the use of fixed non-orthogonal basis functions.
Stats
The FWformer achieves superior accuracy on event-based video datasets. The FWformer reduces theoretical energy consumption by 20%-25%. The FWformer reduces GPU memory usage by 4%-26%.
Quotes
"Our result indicates the continuous refinement of new Transformers, that are inspired either by biological discovery (spike-form), or information theory (Fourier or Wavelet Transform), is promising."

Deeper Inquiries

How can the concept of fixed non-orthogonal basis functions be applied in other neural network architectures

The concept of fixed non-orthogonal basis functions can be applied in other neural network architectures by providing a structured and prior knowledge-based approach to information transformation. These fixed bases can offer a simplified representation of features, reducing computational complexity and enhancing efficiency in various tasks. In architectures beyond visual classification, such as natural language processing (NLP) or speech recognition, incorporating fixed non-orthogonal bases can help in capturing essential patterns and relationships in the data. By using these bases, the network can benefit from a more structured and efficient representation of information, leading to improved performance and reduced computational costs.

What are the implications of the decreasing orthogonality of self-attention bases during training

The implications of the decreasing orthogonality of self-attention bases during training suggest a shift in the nature of information representation within the network. As the bases become less orthogonal, the network adapts to form more complex and intertwined representations of features. This phenomenon indicates that the network is moving towards capturing more intricate relationships and patterns in the data. While orthogonality initially provides a sparse and efficient representation, the decrease in orthogonality allows for a more nuanced and detailed understanding of the data. This evolution in basis functions during training signifies the network's ability to adapt and learn complex relationships over time.

How can the findings of this study be extended to other domains beyond visual classification

The findings of this study can be extended to other domains beyond visual classification by leveraging the concept of fixed non-orthogonal basis functions in various neural network architectures. In tasks such as NLP, speech recognition, or reinforcement learning, where structured and efficient information representation is crucial, incorporating fixed bases can lead to improved performance and reduced computational complexity. By utilizing prior knowledge-based basis functions, networks in these domains can benefit from a more streamlined and effective approach to information transformation. Additionally, the understanding of basis functions evolving from orthogonal to non-orthogonal can be applied to diverse domains to enhance the network's ability to capture complex relationships and patterns in the data.
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