Artificial Inteligence
  • Preface
  • Introduction
  • Machine Learning
    • Linear Algebra
    • Supervised Learning
      • Neural Networks
      • Linear Classification
      • Loss Function
      • Model Optimization
      • Backpropagation
      • Feature Scaling
      • Model Initialization
      • Recurrent Neural Networks
        • Machine Translation Using RNN
    • Deep Learning
      • Convolution
      • Convolutional Neural Networks
      • Fully Connected Layer
      • Relu Layer
      • Dropout Layer
      • Convolution Layer
        • Making faster
      • Pooling Layer
      • Batch Norm layer
      • Model Solver
      • Object Localization and Detection
      • Single Shot Detectors
        • Yolo
        • SSD
      • Image Segmentation
      • GoogleNet
      • Residual Net
      • Deep Learning Libraries
    • Unsupervised Learning
      • Principal Component Analysis
      • Generative Models
    • Distributed Learning
    • Methodology for usage
      • Imbalanced/Missing Datasets
  • Artificial Intelligence
    • OpenAI Gym
    • Tree Search
    • Markov Decision process
    • Reinforcement Learning
      • Q_Learning_Simple
      • Deep Q Learning
      • Deep Reinforcement Learning
    • Natural Language Processing
      • Word2Vec
  • Appendix
    • Statistics and Probability
      • Probability
        • Markov Chains
        • Random Walk
    • Lua and Torch
    • Tensorflow
      • Multi Layer Perceptron MNIST
      • Convolution Neural Network MNIST
      • SkFlow
    • PyTorch
      • Transfer Learning
      • DataLoader and DataSets
      • Visualizing Results
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  • Introduction
  • Inception Layer
  • Caffe Example

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  1. Machine Learning
  2. Deep Learning

GoogleNet

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Last updated 5 years ago

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Introduction

On this chapter you will learn about the googleNet (Winning architecture on ImageNet 2014) and it's inception layers.

googleNet has 22 layer, and almost 12x less parameters (So faster and less then Alexnet and much more accurate.

Their idea was to make a model that also could be used on a smart-phone (Keep calculation budget around 1.5 billion multiply-adds on prediction).

Inception Layer

The idea of the inception layer is to cover a bigger area, but also keep a fine resolution for small information on the images. So the idea is to convolve in parallel different sizes from the most accurate detailing (1x1) to a bigger one (5x5).

The idea is that a series of gabor filters with different sizes, will handle better multiple objects scales. With the advantage that all filters on the inception layer are learnable.

The most straightforward way to improve performance on deep learning is to use more layers and more data, googleNet use 9 inception modules. The problem is that more parameters also means that your model is more prone to overfit. So to avoid a parameter explosion on the inception layers, all bottleneck techniques are exploited.

Using the bottleneck approaches we can rebuild the inception module with more non-linearities and less parameters. Also a max pooling layer is added to summarize the content of the previous layer. All the results are concatenated one after the other, and given to the next layer.

Caffe Example

Bellow we present 2 inception layers on cascade from the original googleNet.