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I am trying to apply an autoencoder for feature extraction with the input like I=[x1,x2,x3,...,xn]. Representing the latent code after encoding as L, I want to improve the influence of one element of the input, such as x1, on L. My intention is that when x2,x3,...,xn remain constant, a small change in x1 can lead to a huge change in the code L. So what kind of autoencoder structure should be adopted to achieve this purpose? Thanks.

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You could add a new term to the loss that enforces precisely what you said: compute a modified version of the batch adding a very small variation to the $x_1$ component, compute the distance $D$ between the latent code of the original batch and the variation and add a new term to the loss in the lines of $L_D = 1/D$.

To combine the original loss and the new term, you can add them together with a weight $L + \alpha L_D$. To determine $\alpha$, you can try with different values, ensuring that the gradients of each term are not too different in magnitude.

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  • $\begingroup$ You could also add weights for the reconstruction of each individual feature $\endgroup$
    – Ggjj11
    Jan 19 at 11:34

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