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Found 12 papers, 10 papers with code
Quantized Spike-driven Transformer
To mitigate this issue, we take inspiration from mutual information entropy and propose a bi-level optimization strategy to rectify the information distribution in Q-SDSA.
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
To address this issue, we first introduce the Spike Voxel Coding (SVC) scheme, which encodes the 3D point clouds into a sparse spike train space, reducing the storage requirements and saving time on point cloud preprocessing.
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
This work enables SNNs to match ANN performance while maintaining the low-power advantage, marking a significant step towards SNNs as a general visual backbone.
Scalable Autoregressive Image Generation with Mamba
On the ImageNet1K 256*256 benchmark, our best AiM model achieves a FID of 2. 21, surpassing all existing AR models of comparable parameter counts and demonstrating significant competitiveness against diffusion models, with 2 to 10 times faster inference speed.
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature.
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
Furthermore, our ALIF neuron model achieves remarkable classification accuracy on MNIST (99. 78\%) and CIFAR-10 (93. 89\%) datasets, demonstrating the effectiveness of learning adaptive thresholds for spiking neurons.
High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost
This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining high performance and low inference energy cost.
Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning
To address this issue, we have designed a Spiking Multiscale Attention (SMA) module that captures multiscale spatiotemporal interaction information.
Event-Driven Learning for Spiking Neural Networks
Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
As the demand for heightened performance in SNNs surges, the trend towards training deeper networks becomes imperative, while residual learning stands as a pivotal method for training deep neural networks.
Tensor Decomposition Based Attention Module for Spiking Neural Networks
Then, in AMC, we exploit the inverse procedure of the tensor decomposition process to combine the three tensors into the attention map using a so-called connecting factor.
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.
Ranked #111 on
Image Classification
on CIFAR-10
