SpineNet - Learning Scale-Permuted Backbone for Recognition and Localization
Offered By: Yannic Kilcher via YouTube
Course Description
Overview
Explore a comprehensive video explanation of the SpineNet paper, which challenges traditional CNN architectures for object detection tasks. Learn about scale-permuted networks, neural architecture search, and how SpineNet improves upon ResNet-FPN models. Discover the innovative approach of using multiple rounds of re-scaling and long-range skip connections to enhance recognition and localization performance. Gain insights into up- and downsampling techniques, ablation studies, and potential future developments like attention routing for CNNs. Understand the significant improvements SpineNet achieves in object detection tasks and its transferability to classification tasks.
Syllabus
- Intro & Overview
- Problem Statement
- The Problem with Current Architectures
- Scale-Permuted Networks
- Neural Architecture Search
- Up- and Downsampling
- From ResNet to SpineNet
- Ablations
- My Idea: Attention Routing for CNNs
- More Experiments
- Conclusion & Comments
Taught by
Yannic Kilcher
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent