79 citations · 235 across the 32 of their papers we have counts for
11 papers · 1 filter
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
Shiting Xiao, Yuhang Li, Youngeun Kim +2
Spiking Neural Networks (SNNs) have emerged as a compelling, energy-efficient alternative to traditional Artificial Neural Networks (ANNs) for static image tasks such as image clas…
Adversarial Detection without Model Information
Abhishek Moitra, Youngeun Kim, Priyadarshini Panda
Prior state-of-the-art adversarial detection works are classifier model dependent, i.e., they require classifier model outputs and parameters for training the detector or during ad…
Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation
Youngeun Kim, Joshua Chough, Priyadarshini Panda
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) because of their sparse, asynchronous, and binary event-drive…
Visual Explanations from Spiking Neural Networks using Interspike Intervals
Youngeun Kim, Priyadarshini Panda
Spiking Neural Networks (SNNs) compute and communicate with asynchronous binary temporal events that can lead to significant energy savings with neuromorphic hardware. Recent algor…
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks
Rachel Sterneck, Abhishek Moitra, Priyadarshini Panda
Neural networks have achieved remarkable performance in computer vision, however they are vulnerable to adversarial examples. Adversarial examples are inputs that have been careful…
Compression-aware Continual Learning using Singular Value Decomposition
Varigonda Pavan Teja, Priyadarshini Panda
We propose a compression based continual task learning method that can dynamically grow a neural network. Inspired from the recent model compression techniques, we employ compressi…