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Found 8 papers, 4 papers with code
UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction
This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task focused on future urban planning, which aims to forecast urban layouts by utilizing information from existing urban layouts and planned change maps.
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.
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
Based on DSSA, we propose a novel spiking Vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters.
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more biological-inspired and energy-efficient manner.
Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
However, training an SNN directly poses a challenge due to the undefined gradient of the firing spike process.
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.
