Search Results for author:
Found 8 papers, 0 papers with code
S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition
In this work, we use CSNNs trained in an unsupervised manner with the Spike Timing-Dependent Plasticity (STDP) rule, and we introduce, for the first time, Spiking Separated Spatial and Temporal Convolutions (S3TCs) for the sake of reducing the number of parameters required for video analysis.
Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks
Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP.
Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition
Implementing this model with unsupervised STDP-based CSNNs allows us to further study the performance of these networks with video analysis.
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition
We compare the performance of this model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann datasets.
Improving STDP-based Visual Feature Learning with Whitening
Experiments on CIFAR-10 show that whitening allows STDP to learn visual features that are closer to the ones learned with standard neural networks, with a significantly increased classification performance as compared to DoG filtering.
Impact of facial landmark localization on facial expression recognition
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks?
Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP
Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware.
Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?
Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures.
