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Without experimental evidence to back me up, I can not answer this with 100% confidence. However, I am fairly certain that this will cause issues depending on the model. U-net is essentially an auto-encoder, and due to the fact that it is all just one big neural network, it is likely it will learn the easiest pattern (as all NN do), and that is to find one ...


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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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Let $n=C*K_w*K_h$. Then you should only need $n$ filters. Not $2^n$ to keep all the information. If you just used the rows of the identity matrix as your filters than your convolution would just be making an exact copy so it definitely wouldn't be throwing away information. On the other hand, there will be a max pooling operation. To simplify the question ...


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See Deep Metric Learning Beyond Binary Supervision in CVPR 2019


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