2 research outputs found
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence
Increasing the model capacity is a known approach to enhance the adversarial
robustness of deep learning networks. On the other hand, various model
compression techniques, including pruning and quantization, can reduce the size
of the network while preserving its accuracy. Several recent studies have
addressed the relationship between model compression and adversarial
robustness, while some experiments have reported contradictory results. This
work summarizes available evidence and discusses possible explanations for the
observed effects.Comment: Accepted for publication at SSCI 202
Traffic Light Recognition using Convolutional Neural Networks: A Survey
Real-time traffic light recognition is essential for autonomous driving. Yet,
a cohesive overview of the underlying model architectures for this task is
currently missing. In this work, we conduct a comprehensive survey and analysis
of traffic light recognition methods that use convolutional neural networks
(CNNs). We focus on two essential aspects: datasets and CNN architectures.
Based on an underlying architecture, we cluster methods into three major
groups: (1) modifications of generic object detectors which compensate for
specific task characteristics, (2) multi-stage approaches involving both
rule-based and CNN components, and (3) task-specific single-stage methods. We
describe the most important works in each cluster, discuss the usage of the
datasets, and identify research gaps.Comment: Accepted for publication at ITSC202