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Table of Contents
Abstract
1. Introduction
2. Related Work
3. Motivation and High Level Considerations
4. Architectural Details
5. GoogLeNet
Summary
The document discusses the Inception architecture, specifically GoogLeNet, proposed for the ImageNet Large-Scale Visual Recognition Challenge 2014. It introduces a deep convolutional neural network design that optimizes resources while increasing network depth and width. The architecture, named Inception, is based on the Hebbian principle and multi-scale processing. The document highlights the importance of efficient network design and its impact on object classification and detection. It also compares Inception with previous architectures and emphasizes the use of sparsity and dimensionality reduction for computational efficiency. The Inception architecture utilizes modules with different convolution sizes and pooling operations to process visual information at various scales. The text details how the architecture addresses computational complexity by reducing dimensions before expensive convolutions and the benefits of using Inception modules at higher layers. The document concludes with insights on the GoogLeNet model used in the ILSVRC 2014 competition, showcasing its performance and efficiency.