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 this http URL 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.
Abstract (translated)
基于传统人工神经网络（ANN）的密集预测模型在执行如图像恢复等任务时需要消耗大量能源。目前，基于脉冲神经网络（SNN）框架的神经网络开始在图像恢复领域崭露头角，特别是因为它们通常使用的能量是具有相同架构的人工神经网络的不到10%。然而，由于采用了启发式梯度下降策略，训练一个SNN比训练ANN要昂贵得多。换句话说，在SNN中，潜在膜信号从稀疏到密集的变化过程非常缓慢，这影响了整个训练过程的收敛性。 为了解决这个问题，我们提出了一种新颖的知识蒸馏技术，称为不对称框架（ANN-SNN）蒸馏，在这种方法中，教师模型是ANN而学生模型是SNN。具体来说，我们将通过ANN学习到的中间特征（特征图）作为线索来指导SNN的训练过程。这种做法不仅加速了SNN的收敛速度，还提升了其最终性能，有效地弥合了SNN在效率方面与ANN在学习能力方面的差距。 大量的实验结果表明，我们设计的基于SNN的图像恢复模型，在参数数量上仅为教师网络的1/300，并且能耗为教师网络的1/50，但在某些去噪任务中的性能可以媲美甚至超过教师网络。
URL
https://arxiv.org/abs/2504.01755
