18395282. IMAGE CLASSIFICATION USING BATCH NORMALIZATION LAYERS simplified abstract (GOOGLE LLC)

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IMAGE CLASSIFICATION USING BATCH NORMALIZATION LAYERS

Organization Name

GOOGLE LLC

Inventor(s)

Sergey Ioffe of Mountain View CA (US)

Corinna Cortes of New York NY (US)

IMAGE CLASSIFICATION USING BATCH NORMALIZATION LAYERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18395282 titled 'IMAGE CLASSIFICATION USING BATCH NORMALIZATION LAYERS

The patent application describes methods, systems, and apparatus for processing images using an image classification system with a batch normalization layer.

  • The system includes a convolutional neural network that generates scores for object categories in a set, representing the likelihood of the image containing objects from those categories.
  • The convolutional neural network consists of multiple layers, including a batch normalization layer that improves the network's performance.
  • The batch normalization layer is positioned between the first convolutional neural network layer and the second neural network layer.
    • Key Features and Innovation:**
  • Utilization of a batch normalization layer in an image classification system.
  • Convolutional neural network architecture for processing images and generating object category scores.
  • Improved performance and accuracy in image classification tasks.
    • Potential Applications:**
  • Object recognition in computer vision systems.
  • Image analysis for medical diagnostics.
  • Autonomous driving systems for object detection.
    • Problems Solved:**
  • Enhancing the accuracy of image classification systems.
  • Improving the performance of convolutional neural networks.
  • Streamlining the process of object recognition in images.
    • Benefits:**
  • Increased efficiency in image processing tasks.
  • Enhanced accuracy in object detection and classification.
  • Potential for applications in various industries requiring image analysis.
    • Commercial Applications:**
  • Enhanced security systems for object detection.
  • Advanced surveillance systems for monitoring and tracking objects.
  • Automation in manufacturing processes for quality control.
    • Questions about Image Classification Systems:**

1. How does the batch normalization layer improve the performance of convolutional neural networks in image classification tasks? 2. What are the potential challenges in implementing this technology in real-world applications?


Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images or features of images using an image classification system that includes a batch normalization layer. One of the systems includes a convolutional neural network configured to receive an input comprising an image or image features of the image and to generate a network output that includes respective scores for each object category in a set of object categories, the score for each object category representing a likelihood that that the image contains an image of an object belonging to the category, and the convolutional neural network comprising: a plurality of neural network layers, the plurality of neural network layers comprising a first convolutional neural network layer and a second neural network layer; and a batch normalization layer between the first convolutional neural network layer and the second neural network layer.

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