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Since you have already tried U-Net. You may look into Siamese Networks (with CNNs for images), they are very well known for computing similarity via deep learning. This is a central idea and can be performed with both text and images. As a tip, you may be able to leverage a lot of architecture from U-Net to Siamese. Hope it helps, Some useful links to ...


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The paper you are citing is the paper that introduced the cascaded convolution neural network. In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the introduction of a new input feature which ...


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Neural networks are easily fooled, provided you know how to fool them. Consider a linear network with an input layer and an output layer, which has an error function E (we don't need hidden layers to show how to fool a network). For a given input image x, E measures the (squared) difference between the network's output y and the desired (correct) output. ...


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