Self-Supervised High-Order Information Bottleneck Learning of Spiking Neural Network for Robust Event-Based Optical Flow Estimation

Abstract

Event cameras form a fundamental foundation for visual perception in scenes characterized by high speed and a wide dynamic range. Although deep learning techniques have achieved remarkable success in estimating event-based optical flow, existing methods have not adequately addressed the significance of temporal information in capturing spatiotemporal features. Due to the dynamics of spiking neurons in SNNs, which preserve important information while forgetting redundant information over time, they are expected to outperform analog neural networks (ANNs) with the same architecture and size in sequential regression tasks. In addition, SNNs on neuromorphic hardware achieve advantages of extremely low power consumption. However, present SNN architectures encounter issues related to limited generalization and robustness during training, particularly in noisy scenes. To tackle these problems, this study introduces an innovative spike-based self-supervised learning algorithm known as SeLHIB, which leverages the information bottleneck theory. By utilizing event-based camera inputs, SeLHIB enables robust estimation of optical flow in the presence of noise. To the best of our knowledge, this is the first proposal of a self-supervised information bottleneck learning strategy based on SNNs. Furthermore, we develop spike-based self-supervised algorithms with nonlinear and high-order information bottleneck learning that employs nonlinear and high-order mutual information to enhance the extraction of relevant information and eliminate redundancy. We demonstrate that SeLHIB significantly enhances the generalization ability and robustness of optical flow estimation in various noise conditions. In terms of energy efficiency, SeLHIB achieves 90.44% and 45.70% cut down of energy consumption compared to its counterpart ANN and counterpart SNN models, while attaining 33.78% lower AEE (MVSEC), 5.96% lower RSAT (ECD) and 6.21% lower RSAT (HQF) compared to the counterpart ANN implementations with the same sizes and architectures.