How can autoencoders help with anomaly detection and data compression?

Autoencoders represent a technique to facilitate machine learning. Firstly, the process called encoder is applied, which generally reduces the dimensionality of the original information, whether it be a video, an image, sound, and so on. The lower-dimensional data undergoes the machine learning process that deals with a representation of the original information. In the decoding stage, the process attempts to restore its original form.

From what I can recall, I believe the encoder processes the input to create the latent code, while the decoder aims to reconstruct the input from this code. By minimizing the reconstruction error, which quantifies the disparity between the input and output data, autoencoders strive to learn meaningful representations and capture essential features of the input data. This fundamental architecture underpins the functionality of autoencoders across various applications, from image and text processing to anomaly detection and data compression, showcasing their versatility and efficacy in extracting relevant information and generating valuable insights from complex datasets.

This is how autoencoders flag anomalies (at a high level): - Autoencoder is trained on a dataset of normal data. - Once trained, autoencoder feeds the encoded version, residing in the latent space, into the decoder and attempts to reconstruct every new data point it encounters. - The reconstruction error for each data point is calculated. A high error signifies a difference between the original data and its output, hinting at an anomaly. Underneath, decoder employs techniques to upscale and introduce non-linearity by leveraging activation functions. By progressively increasing the dimensionality and reintroducing features, the decoder aims to recreate the original data point as accurately as possible from the latent space representation.

From what I remember, leveraging autoencoders for anomaly detection entails training them on normal data and utilizing reconstruction errors to score new data. Higher errors indicate a higher likelihood of anomalies, allowing for the detection of outliers without the need for manual labeling or predefined thresholds. This approach showcases the utility of autoencoders in detecting anomalies effectively and autonomously, providing a valuable tool for identifying irregularities and enhancing data security and integrity in various domains, from fraud detection in finance to fault diagnosis in industrial settings.

Autoencoders can help with anomaly detection by learning the normal patterns in data and then flagging any data points that deviate from those patterns. Autoencoders are trained on a dataset of normal data, and they learn to encode the data into a compressed representation. When new data is presented to the autoencoder, it can then decode the data and compare it to the compressed representation. If the data deviates from the normal patterns, the autoencoder will flag it as an anomaly. The use of data in compressed space also allows the encoder to learn from larger data pool, which can be important for building a comprehensive distribution.

It is very simple. We just collect the normal (healthy) samples and pass onto the autoencoder, with the aim of reconstructing the same in the output. In due process, it learns the reconstruction process and sets a loss for the same. During the testing, when we pass a abnormal (faulty) sample, the autoencoder fails to reconstruct, since it has not learnt about that sample. This results in an erroneous (large) error value. This devious gap makes us to the conclusion that the sample is anomalous. This is how autoencoders are used in anomaly detection and highly imbalanced classification.

To add what have been written, I believe that autoencoders can be employed for data compression by configuring them with a compact latent code to encapsulate crucial input features efficiently. The encoder functions as a compressor by transforming the input data into the compact latent code, while the decoder serves as a decompressor to reconstruct the original data from this code.

The encoding part of the process stores data in a latent space. The latent space representation of the data has fewer features and thus acts as a natural compression. This has great benefits for data compression, security, and reconrstruction

I'd like to also add the fact that comprising a generator and a discriminator, GANs operate in a competitive manner where the generator fabricates synthetic data from random noise vectors to mirror genuine data distribution, while the discriminator distinguishes between real and fake data points. The underlying objective of GANs is to train the generator to deceive the discriminator by generating increasingly realistic data and simultaneously enhance the discriminator's ability to differentiate between real and synthetic data accurately.

GANs combine the benefits of discriminative and generative learning in one shot. Their self-contained setup (generator can create more data) allows GANs to be extremely flexible and adaptable to newer data distributions.

Autoencoders are unsupervised models, which means that they are not trained on labeled data. Instead, they are trained on unlabeled data and learn to reconstruct the input data. GANs, on the other hand, are supervised models, which means that they are trained on labeled data. The generator in a GAN is trained to generate data that looks like the labeled data, and the discriminator is trained to distinguish between real and fake data. Autoencoders are typically used for tasks such as image denoising and compression. GANs are typically used for tasks such as image generation and translation.

Speaking from experience and what I remember, autoencoders are known for their ease of training and stability; however, they may yield blurry or distorted reconstructions. On the other hand, GANs present challenges in training and are susceptible to mode collapse, limiting the diversity of generated samples. Nonetheless, GANs often produce sharper and more diverse outputs. Selecting between these approaches depends on your specific objectives and dataset characteristics. Depending on the desired outcomes, you may opt for autoencoders, GANs, or even a hybrid model that combines the strengths of both techniques.

Both autoencoders and GANs are unsupervised and generative models, but they have a key difference. Autoencoder is an explicit generative model, which aims to identify the distribution of the data in order to generate new data. On the other hand, the GAN is an implicit generative model, which aims to generate data and compare it with the original data. Autoencoder learns the data distribution and GAN learns by comparsion.

I'll be covering both in my free AI newsletter, AI Made Simple. You can join more than 30K readers learn about important ideas in AI in a jargon-free but technical way- https://artificialintelligencemadesimple.substack.com/

I highly recommend to refer to Dr.Jason Brownlee's tutorial series of generative learning. There is a 600 page pdf version available for the same, which gives an in depth knowledge of autoencoder, gans and other generative models.