Adaptive Variational Autoencoders for Outlier Detection in Data Streams: Unterschied zwischen den Versionen

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|betreuer=Edouard Fouché
 
|betreuer=Edouard Fouché
 
|termin=Institutsseminar/2019-03-29
 
|termin=Institutsseminar/2019-03-29
|kurzfassung=Outlier detection targets at the discovery of abnormal data patterns. Typical scenarios are intrusion or fraud detection and predictive maintenance.
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|kurzfassung=Outlier detection targets the discovery of abnormal data patterns. Typical scenarios, such as are fraud detection and predictive maintenance are particularly challenging, since the data is available as an infinite and ever evolving stream. In this thesis, we propose Adaptive Variational Autoencoders (AVA), a novel approach for unsupervised outlier detection in data streams.
Since in real-world settings the data is often available as an infinite and ever evolving stream,  
 
we propose Adaptive Variational Autoencoders (AVA) as novel approach for unsupervised outlier detection in data streams in this thesis .
 
  
First, we introduce a streaming framework for training arbitrary generative models on data streams.
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Our contribution is two-fold: (1) we introduce a general streaming framework for training arbitrary generative models on data streams. Here, generative models are useful to capture the history of the stream. (2) We instantiate this framework with a Variational Autoencoder, which adapts its network architecture to the dimensionality of incoming data.
Generative models are used for sample generation to keep learned knowledge.
 
 
 
Second, we instanciate this framework with a Variational Autoencoder as AVA. To handle data streams in a truely unsupervised manner, AVA adapts its network architecture to the dimensionality of incoming data.
 
  
 
Our experiments against several benchmark outlier data sets show that AVA outperforms the state of the art and successfully adapts to streams with concept drift.
 
Our experiments against several benchmark outlier data sets show that AVA outperforms the state of the art and successfully adapts to streams with concept drift.
 
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Aktuelle Version vom 26. März 2019, 23:19 Uhr

Vortragende(r) Florian Pieper
Vortragstyp Masterarbeit
Betreuer(in) Edouard Fouché
Termin Fr 29. März 2019
Vortragsmodus
Kurzfassung Outlier detection targets the discovery of abnormal data patterns. Typical scenarios, such as are fraud detection and predictive maintenance are particularly challenging, since the data is available as an infinite and ever evolving stream. In this thesis, we propose Adaptive Variational Autoencoders (AVA), a novel approach for unsupervised outlier detection in data streams.

Our contribution is two-fold: (1) we introduce a general streaming framework for training arbitrary generative models on data streams. Here, generative models are useful to capture the history of the stream. (2) We instantiate this framework with a Variational Autoencoder, which adapts its network architecture to the dimensionality of incoming data.

Our experiments against several benchmark outlier data sets show that AVA outperforms the state of the art and successfully adapts to streams with concept drift.