Image segmentation can be used to allocate each pixel in th

driving, video surveillance, face recognition.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 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 Rich People Phone Number List 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 calculated in parallel calculation suitable for efficiently calculating the training and reasoning process of accelerated models on hardware such as GPU. CNN's disadvantages data demand is large CNN usually requires a large number of labeling data to train models, especially on complex tasks and large -scale datasets to obtain good performance. Computing resources require high due to the complexity training and