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I would like to implement the approach represented in this paper. Here they used following reconstruction loss:

$$ L(X)= \frac{\lambda \cdot || M \odot (X - F(\overline{M} \odot X)) ||_{1} + (1 - \lambda) \cdot || \overline{M} \odot (X - F(\overline{M} \odot X)) ||_{1}}{N} $$

Unfortunately, the author does not explain the function $F$. Does someone know a similar function or could understand the function's purpose from the context?

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$F$ in this context is the output of the Convolutional Neural Network that's being trained, which is of the same size as $X$.

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  • $\begingroup$ That makes total sense, because $\hat{y}$ is missing in the equation. Thank you. $\endgroup$
    – oezguensi
    Commented Jul 18, 2019 at 15:22

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