Bridge the Gap between SNN and ANN for Image Restoration

• URL: http://arxiv.org/abs/2504.01755v1
• Date: Wed, 02 Apr 2025 14:12:06 GMT
• Title: Bridge the Gap between SNN and ANN for Image Restoration
• Authors: Xin Su, Chen Wu, Zhuoran Zheng,
• Abstract summary: Currently, neural networks based on the SNN (Spiking Neural Network) framework are beginning to make their mark in the field of image restoration.<n>We propose a novel distillation technique, called asymmetric framework (ANN-SNN) distillation, in which the teacher is an ANN and the student is an SNN.<n>Specifically, we leverage the intermediate features (feature maps) learned by the ANN as hints to guide the training process of the SNN.
• Score: 7.487270862599671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Models of dense prediction based on traditional Artificial Neural Networks (ANNs) require a lot of energy, especially for image restoration tasks. Currently, neural networks based on the SNN (Spiking Neural Network) framework are beginning to make their mark in the field of image restoration, especially as they typically use less than 10\% of the energy of ANNs with the same architecture. However, training an SNN is much more expensive than training an ANN, due to the use of the heuristic gradient descent strategy. In other words, the process of SNN's potential membrane signal changing from sparse to dense is very slow, which affects the convergence of the whole model.To tackle this problem, we propose a novel distillation technique, called asymmetric framework (ANN-SNN) distillation, in which the teacher is an ANN and the student is an SNN. Specifically, we leverage the intermediate features (feature maps) learned by the ANN as hints to guide the training process of the SNN. This approach not only accelerates the convergence of the SNN but also improves its final performance, effectively bridging the gap between the efficiency of the SNN and the superior learning capabilities of ANN. Extensive experimental results show that our designed SNN-based image restoration model, which has only 1/300 the number of parameters of the teacher network and 1/50 the energy consumption of the teacher network, is as good as the teacher network in some denoising tasks.

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