Application scenario CNN has a

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wide range of applications in the field of computer vision The following is some CNN's application scenario image classification can divide images into different categories such as identifying handwriting numbers, identification animals, and identification objects Target detection can position and identify multiple targets in images This has important applications in the fields of autonomous driving, video surveillance, face recognition Image segmentation can be used to allocate each pixel in the image to be assigned to different categories This is widely used in medical image analysis and natural language processing Image generation


can be used for image generation, such as generating realistic images, image style conversion, image super resolution, etc Video analysis can be used for video analysis such as action recognition, behavior recognition, video content understanding, etc Medical image analysis can be used for Rich People Phone Number List medical image analysis such as pathological image recognition, lung nodule detection, and disease prediction 5 The advantages and disadvantages of CNN can





capture the local characteristics of image and voice in the local spatial relationship of the input data through convolution operations Parameter sharing the convolution nuclear in CNN sharing parameters throughout the input data, so that the number of parameters of the network can greatly reduce the risk of overfitting and improve the training efficiency of the model The transition invariably CNN has a translation of transition, which means that the translation operation of the input data will not change the output of the network This makes CNN have a certain robustness when processing data such as images Multi -level features CNN can learn more abstract and advanced features by stacking multiple convolutional layers and pooling layers to improve the expression ability of models Parallel computing CNN convolution operations can be