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To the best of my knowledge, there is no consistent answer to the first question. It's like asking "is it better to use (3,3) kernel size or (5,5) kernel size?". As far as I know, the reason behind choosing either an even or odd kernel comes from trial and error in practical implementations and can vary from one case to the other. For the second ...


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What loss function are you using? When you want to perfectly overfit, you should use L2 and not L1 loss. The reason for this is that the derivative for the L2 loss is $\frac{d l^2} {d l} = 2l$ and the derivative for the L1 loss is $\frac{dl^1}{d l} = 1$. This means that the gradient update for the L2 loss gets smaller as the loss value decreases, therefore ...


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Depending on what you are trying to do with your CNN, regularization may indeed make sense. Pruning your network by regularization to make it sparse has two main advantages: It simplifies the network, making training and computation faster and easier; It prevents overfitting, and allows to make sure your network will generalize well on new data. An ...


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You have overfitted the training set. Try again with more data, or with some form of regularization, possibly including added noise.


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