Search Results for author:
Found 16 papers, 9 papers with code
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks
In this paper, we propose Hybrid Step-wise Distillation (HSD) method, tailored for neuromorphic datasets, to mitigate the notable decline in performance at lower time steps.
Robust Stable Spiking Neural Networks
We present that membrane potential perturbation dynamics can reliably convey the intensity of perturbation.
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
In this paper, we propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization.
Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs
One of the mainstream approaches to implementing deep SNNs is the ANN-SNN conversion, which integrates the efficient training strategy of ANNs with the energy-saving potential and fast inference capability of SNNs.
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $\ell_1$ norm of the model's gradient with respect to the input image.
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.
Spike timing reshapes robustness against attacks in spiking neural networks
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks.
SpikeCV: Open a Continuous Computer Vision Era
SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes.
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs.
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips.
Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) have attracted significant attention due to their energy-efficient properties and potential application on neuromorphic hardware.
Optimized Potential Initialization for Low-latency Spiking Neural Networks
We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps.
Accelerating Training of Deep Spiking Neural Networks with Parameter Initialization
Despite that spiking neural networks (SNNs) show strong advantages in information encoding, power consuming, and computational capability, the underdevelopment of supervised learning algorithms is still a hindrance for training SNN.
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference.
A Fixed point view: A Model-Based Clustering Framework
With the inflation of the data, clustering analysis, as a branch of unsupervised learning, lacks unified understanding and application of its mathematical law.
