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CNN such that outputs the image with the feature added to the input image can be created? For example, if an image of a person's face is input, outputs an image of the person's face wearing glasses.
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they generate images by a se- ries convolution and upsampling operations) since this is by far the most common design for generative CNNs.
The Convolutional Neural Network (CNN or ConvNet) is a subtype of the Neural Networks that is mainly used for applications in image and speech recognition. Its built-in convolutional layer reduces the high dimensionality of images without losing its information. That is why CNNs are especially suited for this use case.
The procedure is simple: Perform a forward pass on each image to extract the features at a desired network layer. Create an invisible grid overlay on each image where the number of cells is equal to the dimension of the extracted features.
The true fact is that CNNs provide automatic feature extraction, which is the primary advantage [2]. The specified input data is initially forwarded to a feature extraction network, and then the resultant extracted features are forwarded to a classifier network as shown in Fig. 7.8.
There are several options but basically the same way that you have one input for every pixel you must have one output from every pixel in the output image.
In MLPs you must have the same neurons in the input layer than in the output layer.
In CNNs you can also have at the beginning convolutional layers and after that deconvolutional layers.
Take a look at this paper (it is awesome) to create very realistic images from other images (for example satellite and map views in google maps). It is a neural network that is trying to solve the problem and also trying to create images that other neural network is not capable to distinguish from real images (it also have the source code available):
https://phillipi.github.io/pix2pix/
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