<blockquote>
<p>What can be a similar criteria that we can use for pseudo labelling if we use a clustering method instead of an Autoencoder?</p>
</blockquote>
<p>Maybe the amount of Data points inside a Cluster. Like you said, an anomaly is a sparse occurrence, so there should be fewer data points in an anomalous cluster.</p>
<p>This would only work for known anomalies, which you can put in a known anomalous cluster, though.</p>

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2022-10-13T15:34:38.383Z
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2022-10-13T14:01:10.517Z
2022-10-13T14:01:10.517Z
Skobo Do
<p>I'm working on Unsupervised Video Anomaly Detection, and I've tried implementing the Generative Cooperative Learning method, with the help of <a href="https://arxiv.org/abs/2203.03962" rel="nofollow noreferrer">this</a> paper.</p>
<p>The method uses a fixed backbone (ResNext-101) for video feature extraction. The videos are divided into segments of 16 frames, and a feature vector is computed for each segment. A generator (A simple Autoencoder) provides pseudo labels (based on the reconstruction error) for the discriminator which is a simple fully connected classifier. Pseudo labels from the discriminator are used to improve the generator using a process called negative learning, and in this fashion, the Generator and Discriminator are put in a collaborative learning loop, and the loss eventually converges.</p>
<p>I've recently come across the <a href="https://openaccess.thecvf.com/content_ECCV_2018/papers/Mathilde_Caron_Deep_Clustering_for_ECCV_2018_paper.pdf" rel="nofollow noreferrer">Deep Clustering</a> paper , and was wondering if we can use a clustering method instead of the autoencoder as part of the generator. I think we can use the cosine distance as a good distance metric. The troublesome part however, is <strong>thinking of a good criteria for generating the pseudo labels.</strong></p>
<p>With the autoencoder, the reconstruction error is a pretty intuitive criteria for pseudo labelling. Since anomalies are sparse, the autoencoder will not be able to reconstruct them properly and so they will have large(r) reconstruction errors.</p>
<p><strong>What can be a similar criteria that we can use for pseudo labelling if we use a clustering method instead of an Autoencoder?</strong></p>

<deep-learning><computer-vision><unsupervised-learning><clustering><self-supervised-learning>
Deep Clustering Approach for Unsupervised Video Anomaly Detection
satan 29
# Deep Clustering Approach for Unsupervised Video Anomaly Detection

(Asked by: username_0 on 2022-10-13)

I'm working on Unsupervised Video Anomaly Detection, and I've tried implementing the Generative Cooperative Learning method, with the help of [this](https://arxiv.org/abs/2203.03962) paper.

The method uses a fixed backbone (ResNext-101) for video feature extraction. The videos are divided into segments of 16 frames, and a feature vector is computed for each segment. A generator (A simple Autoencoder) provides pseudo labels (based on the reconstruction error) for the discriminator which is a simple fully connected classifier. Pseudo labels from the discriminator are used to improve the generator using a process called negative learning, and in this fashion, the Generator and Discriminator are put in a collaborative learning loop, and the loss eventually converges.

I've recently come across the [Deep Clustering](https://openaccess.thecvf.com/content_ECCV_2018/papers/Mathilde_Caron_Deep_Clustering_for_ECCV_2018_paper.pdf) paper , and was wondering if we can use a clustering method instead of the autoencoder as part of the generator. I think we can use the cosine distance as a good distance metric. The troublesome part however, is **thinking of a good criteria for generating the pseudo labels.**

With the autoencoder, the reconstruction error is a pretty intuitive criteria for pseudo labelling. Since anomalies are sparse, the autoencoder will not be able to reconstruct them properly and so they will have large(r) reconstruction errors.

**What can be a similar criteria that we can use for pseudo labelling if we use a clustering method instead of an Autoencoder?**

**Tags:** deep-learning, computer-vision, unsupervised-learning, clustering, self-supervised-learning


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## 1 answers

### Answer by username_1 on 2022-10-13. Score: 1

> What can be a similar criteria that we can use for pseudo labelling if we use a clustering method instead of an Autoencoder?

Maybe the amount of Data points inside a Cluster. Like you said, an anomaly is a sparse occurrence, so there should be fewer data points in an anomalous cluster.

This would only work for known anomalies, which you can put in a known anomalous cluster, though.

> **Comments:**
> **username_2**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  

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