<p><span class="math-container">$F$</span> in this context is the output of the Convolutional Neural Network that's being trained, which is of the same size as <span class="math-container">$X$</span>.   </p>

CC BY-SA 4.0
2019-07-18T15:22:10.760Z
That makes total sense, because $\hat{y}$ is missing in the equation. Thank you.
oezguensi
CC BY-SA 4.0
2019-07-18T15:13:04.387Z
2019-07-18T18:03:08.133Z
2019-07-18T18:03:08.133Z
mshlis
<p>I would like to implement the approach represented in <a href="https://arxiv.org/pdf/1811.06861.pdf" rel="nofollow noreferrer">this</a> paper. Here they used following reconstruction loss:</p>

<p><span class="math-container">$$
L(X)= \frac{\lambda \cdot || M \odot (X - F(\overline{M} \odot X)) ||_{1} + (1 - \lambda) \cdot || \overline{M} \odot (X - F(\overline{M} \odot X)) ||_{1}}{N}
$$</span></p>

<p>Unfortunately, the author does not explain the function <span class="math-container">$F$</span>.
Does someone know a similar function or could understand the function's purpose from the context?</p>

<convolutional-neural-networks><papers><anomaly-detection>
Understanding the reconstruction loss in the paper "Anomaly Detection using Deep Learning based Image Completion"
oezguensi
# Understanding the reconstruction loss in the paper "Anomaly Detection using Deep Learning based Image Completion"

(Asked by: username_0 on 2019-07-18)

I would like to implement the approach represented in [this](https://arxiv.org/pdf/1811.06861.pdf) 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?

**Tags:** convolutional-neural-networks, papers, anomaly-detection


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## 1 answers

### Answer by username_1 on 2019-07-18. Score: 1 | Accepted answer

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

> **Comments:**
> **username_0**: That makes total sense, because $\hat{y}$ is missing in the equation. Thank you.  

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