When trained in an end-to-end fashion, the hidden layers of the network learn filters that are robust and even capable of denoising the input data. The decoder then attempts to reconstruct the input data from the latent space. The encoder accepts the input data and compresses it into the latent-space representation. To accomplish this task, an autoencoder uses two components: an encoder and a decoder. Reconstruct the input data from the latent representation.Internally compress the data into a latent-space representation.How can deep learning and autoencoders be used for anomaly detection?Īs I discussed in my intro to autoencoder tutorial, autoencoders are a type of unsupervised neural network that can: The answer is yes - but you need to frame the problem correctly. To detect anomalies, machine learning researchers have created algorithms such as Isolation Forests, One-class SVMs, Elliptic Envelopes, and Local Outlier Factor to help detect such events however, all of these methods are rooted in traditional machine learning.Ĭan deep learning be used for anomaly detection as well? ![]() The problem is only compounded by the fact that there is a massive imbalance in our class labels.īy definition, anomalies will rarely occur, so the majority of our data points will be of valid events. Defective items in a factory/on a conveyor beltĭepending on your exact use case and application, anomalies only typically occur 0.001-1% of the time - that’s an incredibly small fraction of the time.Large dips and spikes in the stock market due to world events.To quote my intro to anomaly detection tutorial:Īnomalies are defined as events that deviate from the standard, happen rarely, and don’t follow the rest of the “pattern.” Figure 1: In this tutorial, we will detect anomalies with Keras, TensorFlow, and Deep Learning ( image source).
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