How do you use Autoencoders for anomaly detection?
Anomaly detection using Autoencoders
- During the training, input only normal transactions to the Encoder.
- The Decoder will use the bottleneck layers output to reconstruct the normal transactions of the original input data.
- A fraudulent transaction will be different from a normal transaction.
Why it is better to use convolutional autoencoders when we are dealing with images?
The best known neural network for modeling image data is the Convolutional Neural Network (CNN, or ConvNet). It can better retain the connected information between the pixels of an image. The particular design of the layers in a CNN makes it a better choice to process image data.
Why are outliers revealed in autoencoder anomaly detection?
The answer is once the main patterns are identified, the outliers are revealed. Many distance-based techniques (e.g. KNNs) suffer the curse of dimensionality when they compute distances of every data point in the full feature space. High dimensionality has to be reduced.
How is an autoencoder used in a neural network?
As I discussed in my intro to autoencoder tutorial, autoencoders are a type of unsupervised neural network that can: Internally compress the data into a latent-space representation Reconstruct the input data from the latent representation To accomplish this task, an autoencoder uses two components: an encoder and a decoder.
How does an autoencoder reconstruct the latent space?
Reconstruct the input data from the latent representation To accomplish this task, an autoencoder uses two components: an encoder and a decoder. The encoder accepts the input data and compresses it into the latent-space representation. The decoder then attempts to reconstruct the input data from the latent space.
Why does autoencoder have high MSE when reconstructing elephant?
Since the autoencoder has never seen an elephant before, and more to the point, was never trained to reconstruct an elephant, our MSE will be very high. If the MSE of the reconstruction is high, then we likely have an outlier. Alon Agmon does a great job explaining this concept in more detail in this article.