Helping understanding paper FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Miguel_Campos: <p>Hello Family.</p>
<p>I am working on this paper <a href="https://arxiv.org/abs/2111.07677" rel="noopener nofollow ugc">FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows</a></p>
<p>I have some question that will post here.<br>
The first question is about what two paragraphs say</p>
<blockquote>
<p>For ResNet, we directly use the features of the last layer in the first three blocks, and<br>
put these features into three corresponding FastFlow model.<br>
…<br>
…<br>
For ResNet18 and Wide-ResNet50-2, we directly use the features of the last layer in the first three blocks, put these features into the 2D flow model to obtain their respective anomaly de-<br>
tection and localization results, and finally take the average value as the final result.</p>
</blockquote>
<p>Focusing on the feature level that i need to extract, do I need to extract one or three features levels?<br>
I understood to extract one feature which implementation looks like this:</p>
<blockquote>
<p>self.feature_extractor = models.wide_resnet50_2(True)<br>
self.feature_extractor = create_feature_extractor(<br>
self.feature_extractor,<br>
{‘layer1’: ‘Feat’}<br>
)</p>
</blockquote>

AndresJGUTP: <p>Hello Miguel, did you solve it? I am working on this paper too</p>

Miguel_Campos: <p>I am still working on it.</p>
<p>Are needed 3 NF and layers [1, 2, 3] from resnet. Cheers</p>

AndresJGUTP: <p>Do you know which is the loss function? I can’t identify it in the paper</p>
<p>Thank you.</p>

Miguel_Campos: <p>Hello Andres. I highly recommend checking this discussion</p>
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        opened <span class="discourse-local-date" data-format="ll" data-date="2022-02-09" data-time="09:58:56" data-timezone="UTC">09:58AM - 09 Feb 22 UTC</span>
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    <p class="github-body-container">Hello.

I would like to open this issue to talk about this project. I am also <span class="show-more-container"><a href="" rel="noopener" class="show-more">…</a></span><span class="excerpt hidden">interested in developing this project and would be great to share information as the paper doesn't give deeply information about the implementations and offical code is no available.

If you are agree with this iniciative, firstly we could simplify the project to use Wide-ResNet50 in order to get comparative results with the previous researching. I would like to start from the begining of the paper when says:

&gt; For ResNet, we directly use the features of the last layer in the **first three blocks**, and put these features into **three corresponding FastFlow model**.

This make me thing that in the implementation we need to use the features after the input layer, layer 1 and layer 2. In this way this table 6 makes sense

&gt;![image](https://user-images.githubusercontent.com/37028633/153172893-5be71b94-6038-4819-925c-3b37426e518a.png)

But can not to imagine how to concatenate this information for make it sense with the next

&gt; In the forward process, it takes the feature map from the backbone network as input
&gt;![image](https://user-images.githubusercontent.com/37028633/153175764-f6f3115a-f0ea-424f-b76a-7de7f72fa6e5.png)

Depending of what part you read, it seems that just one feature map or 3 are taken</span></p>
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