3 accepted papers
Brain-inspired spiking neural networks (SNNs) provide energy-efficient computation through event-driven processing. However, the shared weights across multiple timesteps lead to serious temporal feature redundancy, limiting both efficiency and performance. This issue is further aggravated when proce…
In recent years, a number of lightweight single-image super-resolution (SISR) network methods heave been proposed. However, most existing approaches do not make full use of the information before and after the convolution and the high-frequency information of the image. In this paper, we propose a l…
Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we pr…