Google llc (20240249138). IMAGE CLASSIFICATION USING BATCH NORMALIZATION LAYERS simplified abstract

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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 20240249138 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 different object categories in an image.
  • The convolutional neural network consists of multiple layers, including a batch normalization layer.
  • The batch normalization layer helps improve the training speed and stability of the neural network.

Potential Applications:

  • Image recognition and classification in various industries such as healthcare, security, and autonomous vehicles.
  • Enhancing the accuracy and efficiency of image processing tasks in research and development.

Problems Solved:

  • Improving the performance and reliability of image classification systems.
  • Addressing issues related to overfitting and training instability in neural networks.

Benefits:

  • Increased accuracy in identifying objects in images.
  • Faster training times for neural networks.
  • Enhanced performance in real-world applications.

Commercial Applications:

  • This technology can be utilized in industries such as e-commerce for product recognition, in healthcare for medical image analysis, and in security for surveillance systems.

Prior Art:

  • Researchers can explore prior art related to image classification systems, convolutional neural networks, and batch normalization techniques in the field of computer vision.

Frequently Updated Research:

  • Stay updated on advancements in convolutional neural networks, image processing algorithms, and batch normalization techniques to enhance the performance of image classification systems.

Questions about Image Classification Systems: 1. How does the batch normalization layer improve the performance of convolutional neural networks in image classification? 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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