AI Atlas:
Variational Autoencoders and AI Creativity
Rudina Seseri
Generative AI has revolutionized enterprise operations, unlocking incredible capabilities such as the creation of lifelike images and videos and the summarization of vast amounts of knowledge across practical use cases. However, these models do not start entirely from scratch – for example, at a fundamental level, an LLM like ChatGPT is recognizing patterns from human text and then using those patterns to create answers to novel situations. The innovation lies in how these models learn which details are important and the complexity of the patterns they can extrapolate.
But what is the threshold for an AI to create something “new?” What other models are capable of doing so?
In today’s AI Atlas, I explore Variational Autoencoders (VAEs), which laid the groundwork for Generative AI by demonstrating how data can be compressed and then transformed into entirely new outputs. These models are still in use today thanks to their versatility across unstructured data and to recent innovations that continue to expand on their capabilities. For example, recent research integrating VAEs with other generative models, such as diffusion models, has even led to improved performance in generating high-quality synthetic data, or artificially created information used to train AI models by mirroring real-world distributions.
🗺️ What are Variational Autoencoders?
Variational Autoencoders (VAEs) are a type of artificial intelligence (AI) model that can learn to generate new data similar to the data it was trained on. These models start with a regular autoencoder, which is designed to compress input data down into essential features then reconstruct the original input from this compressed representation. For example, imagine taking a piece of paper and folding it into a small compact shape. An autoencoder would take this condensed form and reconstruct the original shape – in essence, learning to reconstruct data from a compressed version.
A VAE, on the other hand, learns not just to compress and reconstruct data but also to generate new, similar data. Imagine taking the origami and unfolding it into an entirely new shape, distinct from the original sheet of paper. It does this by first summarizing the original data’s underlying distribution. VAEs then introduce some randomness into the encoding process, which helps in generating new data points that are similar to the original data.
🤔 What is the significance of Variational Autoencoders and what are their limitations?
The development of VAEs introduced a new way of understanding and generating data. Traditional AI models such as Convolutional Neural Networks, which are used to identify and classify images, are generally limited to inferences based on existing data. VAEs, on the other hand, are able to create new data, allowing businesses to explore innovative solutions and applications that were previously difficult or impossible. This innovation made VAEs a cornerstone of the early days of Generative AI, where they still find application today, thanks to strengths such as:
Adaptability to complex data: VAEs are capable of learning meaningful representations without requiring large amounts of labeled training data, making them valuable for unsupervised or semi-supervised learning tasks where human intervention is less available.
Statistics-based processing: VAEs incorporate an element of randomness into their generation process, which improves the diversity of outputs, resulting in greater flexibility towards producing realistic outputs. This is also particularly useful for situations where measuring uncertainty is necessary, such as behavior analysis or demand forecasting.
Robust method of learning: VAEs are effective in learning essential features and patterns within data, providing a deeper understanding and more accurate predictions compared to simpler models like classical regression.
However, Generative AI is not a monolith and VAEs are just one building block in a wider system. Alternative AI architectures such as transformers have been used to compensate for the limitations of VAEs, such as:
Model collapse: VAEs are prone to mode collapse, wherein the model fails to capture the full diversity of a data distribution and generates a limited range of outputs.
Quality of generated data: While VAEs can generate new data, the quality and realism of this data typically has imperfections or lacks fine details when compared to more detail-oriented systems such as Diffusion Models.
Interpretability: The internal workings of VAEs can be difficult to interpret and understand because the compression stage reduces data into only its most essential details. This makes it challenging to explain how VAEs make certain decisions or generate specific outputs.
🛠️ Applications of Variational Autoencoders
VAEs are best used for generating new data, detecting anomalies, and simplifying complex datasets. This makes them useful for improving machine learning models and making data analysis more efficient in use cases such as:
File compression: VAEs can compress content such as video frames into smaller file sizes, which is beneficial for efficient storage and transmission without losing quality.
Financial monitoring: VAEs are employed to detect unusual transactions by identifying data points that do not match standard patterns, helping in fraud detection and risk management.
Factory maintenance: By analyzing sensor data from machinery, VAEs can help identify deviations from normal operation, reducing downtime, and improving maintenance schedules.
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