Perception and Learning for Autonomous Driving - Part 1
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Intro
Robust Multi-modal Perception
Sensor Suite
Perception and Learning for Autonomous Driving
Deep Neural Networks / DNNS
Convolutional networks
Convolutional Neural Networks
Single Layer Architecture
Deep Learning
Classification, Detection, and Segmentation
Architectures Evolution
Factorized convolution
Parameters and computation
Residual Networks
Classification Detection, and Segmentation
Challenges of object detection?
Conceptual approach: Sliding window detection
PASCAL VOC Challenge (2006-2012)
R-CNN details
Fast R-CNN training
Multi-task loss
Taught by
Institute for Pure & Applied Mathematics (IPAM)