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SpineNet - Learning Scale-Permuted Backbone for Recognition and Localization

Offered By: Yannic Kilcher via YouTube

Tags

Neural Networks Courses Machine Learning Courses Convolutional Neural Networks (CNN) Courses Neural Network Architecture Courses ResNet Courses Neural Architecture Search Courses

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

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