All Questions
5 questions
2votes
2answers
282views
Autoencoders are fitting anomalies too good
I have a set of ~ 5000 greyscale images with resolution of 64x128. I want to do an unsupervised anomaly detection. As a first try, I chose convolutional autoencoders (AE) and trained an AE model. I ...
0votes
1answer
368views
Incremental learning on Autoencoder for anomaly detection
I want to incrementally train my pre-trained autoencoder model on data being received every minute. Based on this thread, successive calls to model.fit will incrementally train the model. However, the ...
- 11
-1votes
1answer
68views
Does Anomaly Detection Algorithm works when the features are not correlated?
I am working on an Anomaly Detection Problem and the algorithm I used is an Autoencoder Multivariate Gaussian. The problem with my data is that it is unlabeled and not correlated. For example, let's ...
2votes
0answers
135views
Can autoencoders take time series into account?
Here, I read the following: The first key to understanding is that HTM relies on data that streams over time (...) By contrast, conventional deep learning uses static data and is therefore time ...
- 570
1vote
1answer
5kviews
Why use Variational Autoencoders VAE instead of Autoencoders AE in Anomaly Detection?
I have read many papers that recommend using Variational Autoencoders over Autoencoders since they have a more probabilistic approach and the ability to use KL divergence on the latent dimension. But ...
