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Found 6 papers, 2 papers with code
Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching
However, the unconventional visual signals of these cameras pose a great challenge to the robustness of spiking neural networks.
Frequency and Scale Perspectives of Feature Extraction
Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction.
Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons
Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time.
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments
Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model.
Efficient CNN Architecture Design Guided by Visualization
Our VGNetG-1. 0MP achieves 67. 7% top-1 accuracy with 0. 99M parameters and 69. 2% top-1 accuracy with 1. 14M parameters on ImageNet classification dataset.
CE-FPN: Enhancing Channel Information for Object Detection
Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction.
