The 5 Different Types of Generative Models

In recent times, there has been a significant increase in the popularity of generative models. These innovative algorithms play a crucial role in effectively managing situations where data might be missing and effectively processing sequences of varying lengths. More precisely, generative models are proficient in dealing with the representations of distributions that are defined over data points in potentially intricate and high-dimensional spaces.

This essay presents an in-depth exploration of five prominent types of generative models. Each model’s underlying principles, strengths, and applications are discussed to highlight their significance in advancing the field of artificial intelligence.

“Our intelligence is what makes us human, and AI is an extension of that quality. Artificial intelligence is extending what we can do with our abilities. In this way, it’s letting us become more human.”

– Yann LeCun.

This class of machine learning models predicts the probability distribution of each element in a sequence, leveraging information from the previous elements. Well known for its flexibility, interpretability, and capacity to capture temporal dependencies, the autoregressive approach is particularly effective with sequential data like time series, natural language, and speech and is widely used in various domains.

Based on recurrent neural networks (RNNs) or transformers, these language models have demonstrated state-of-the-art performance in tasks like text generation and machine translation. The model’s performance depends on the appropriate selection of the order (p) and the choice of the function (f) that represents the relationships between the variables. One common approach to training these models is using maximum likelihood estimation (MLE) to observe the data given the model parameters. Consequently, the optimal model parameters are those that maximize the likelihood function.

They find applications in a wide range of fields due to their ability to capture sequential dependencies and patterns. Some common applications include:

• Time Series Forecasting: They are widely used for predicting future values in time series data, making them valuable in financial forecasting, weather prediction, and demand forecasting. Time series models find extensive application in diverse fields, ranging from sales forecasting to weather forecasting, owing to their versatility and ability to tackle uncertainties in future predictions. When faced with uncertainty, utilizing a time series model becomes a highly effective approach for accurate forecasting.
• Natural Language Processing: Language modeling is a common application, enabling the prediction of the next word in a sentence based on the preceding words. This capability facilitates tasks like text generation and machine translation.
• Speech Recognition: They have been used to model the correlations between adjacent phonemes or acoustic features.
• Audio Processing: They can be employed for tasks such as speech synthesis, denoising, and audio compression.

Source: Guide to Autoregressive Models

GGMs are a subset of generative models that assume the data is drawn from a Gaussian distribution. They estimate the parameters of this distribution to generate new data points. Notable examples include Gaussian Mixture Models (GMMs). These models are particularly useful for clustering, density estimation, and anomaly detection tasks.

Testing. CI/CD. Monitoring.

Because ML systems are more fragile than you think. All based on our open-source core.

Install Open SourceBook a Demo

GMMs are a method for representing a dataset as a combination of several Gaussian distributions. The model consists of individual Gaussian components and mixing weights, where the components represent the underlying Gaussian distributions, and the mixing weights determine their contributions to the overall distribution. The goal of GMMs is to estimate the parameters describing the mixture components and mixing weights from the given data, achieved through the Expectation-Maximization (EM) algorithm. Once trained, GMMs can be used for various tasks, including clustering and data generation.

Training GMMs typically involves using the Expectation-Maximization (EM) algorithm, an iterative optimization technique. The EM algorithm alternates between the Expectation (E) step, where it estimates the posterior probabilities of each data point belonging to each Gaussian component, and the Maximization (M) step, where it updates the parameters of the Gaussian components based on these probabilities.

Let’s consider an example of using GMMs, for clustering customer data in a retail setting. Imagine a retail company that wants to segment its customer base to gain insights into different customer groups based on their shopping preferences. The company collects transaction data, including the total amount spent and the number of items purchased by each customer over a specific period. The GMM is trained on preprocessed transaction data, including total amount spent and number of items purchased. The model identifies three customer segments: frequent high-spenders, occasional moderate-spenders, and low-spenders. Each cluster represents distinct groups with different shopping behaviors. This segmentation enables the company to tailor marketing strategies and promotions, improving customer satisfaction and optimizing business decisions.

Source: Machine Learning based Customer Intelligence should be at the core of Retail Technology strategy!

As a class of machine learning models, Probabilistic Generative Models focus on modeling the underlying probability distribution of the data. Their objective is to capture the relationship between input features and target labels while explicitly modeling the uncertainty in the data. At the core of these models lies the concept of probability distribution, which helps to understand how the input data is generated from a probabilistic standpoint, as well as estimate the joint probability distribution of input features and target labels, allowing for a more comprehensive understanding of the data generation process.

The learning process involves estimating the model parameters that best fit the observed data. Common techniques for parameter estimation include MLE and Bayesian inference. They offer a broad spectrum of applications:

Image Generation: Models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) can generate new images by sampling from the learned probability distribution, enabling realistic image synthesis and creative content generation. In this context, the VAE is designed with an encoder that maps data into a latent space while the decoder generates new data points from samples in that space.

Source: Typical Generative Adversarial Networks (GAN) architecture

Natural Language Processing: Used in language modeling tasks, such as machine translation, text generation, and sentiment analysis. They capture the probability distribution of words and sentences in a language.

Collaborative Filtering: Recommendation systems can predict user preferences by modeling the probability distribution of user-item interactions, providing personalized recommendations.

Anomaly Detection: The anomalies in data can be detected by comparing the likelihood of new samples with the learned data distribution. Unusual data points that deviate significantly from the model’s distribution are flagged as anomalies.

Suppose we examine an illustrative scenario of using a probabilistic generative model, specifically a VAE, for image generation. A company wants to generate high-quality and realistic images of different types of flowers to use in its e-commerce platform. However, they have a limited dataset of flower images, and they want to generate new images to expand their inventory. The VAE consists of an encoder and decoder, learning a compact latent space representation of the flower images. The model, for instance, is trained with a reconstruction loss and Kullback-Leibler divergence to ensure accurate image generation and a smooth latent space. By sampling from the learned latent space, the VAE generates diverse and realistic flower images, allowing the company to expand its inventory and improve the user experience on its platform. These models enable efficient and creative data generation for various business applications.

Hidden Markov Model (HMM) is a statistical model widely used in various fields, especially in sequential data analysis and time series modeling. HMMs are designed to model systems that are assumed to be Markovian, meaning that the future state of the system depends only on the current state and is independent of previous states.

HMMs consist of two main components: hidden states and observable states. The hidden states represent the underlying, unobservable states of the system, while the observable states are the states we can directly observe. The transitions between hidden states are governed by a set of probabilities known as transition probabilities, while the emissions from hidden states to observable states are controlled by emission probabilities. HMMs have found applications in diverse domains:

• Speech Recognition: HMMs are widely used in automatic speech recognition systems to model the correlations between phonemes and acoustic features. By efficiently modeling the sequential dependencies between words and their corresponding part-of-speech tags, HMMs provide a principled approach for automating linguistic analysis, contributing to the advancement of natural language understanding and processing applications.
• Natural Language Processing: HMMs have been applied in tasks like part-of-speech tagging and named entity recognition.
• Bioinformatics: HMMs are used to analyze biological sequences, such as DNA and protein sequences, for gene prediction and protein family classification.
• Financial Modeling: HMMs can model financial time series data for tasks like market analysis and prediction.

Flow-based models are generative models that directly model the data distribution through a series of invertible transformations, i.e., a set of mathematical operations that can be both applied and reversed without any loss of information. The core idea of flow-based models lies in the concept of normalizing flows. A normalizing flow consists of a sequence of invertible transformations, often represented as a series of layers. These transformations gradually shape the simple initial distribution into the target data distribution. Flow-based models can capture complex data distributions by chaining multiple invertible transformations together. These models provide tractable likelihood estimation and efficient sampling, making them highly desirable for various applications, including image generation, density estimation, and anomaly detection.

The transformations used in flow-based models are bijective, meaning they have an inverse, enabling both forward and inverse computations. These invertible transformations allow for exact likelihood computation and efficient sampling. Training flow-based models involves maximizing the likelihood of the observed data. Since flow-based models provide tractable likelihoods, MLE can be directly computed during training. The training process typically involves optimizing the parameters of the invertible transformations using gradient-based optimization methods.

Unlike other generative models like VAEs or GANs, flow-based models can compute the exact likelihood of observed data, allowing for accurate density estimation.

Source: Comparison of three categories of generative models

To conclude, the diverse and powerful presented types of generative AI offer boundless opportunities for creativity and problem-solving in various fields. Having in mind that generative AI has the potential to transform creative industries and enhance human-machine interactions by enabling AI systems to generate content that is indistinguishable from human-created data, generative models have emerged as a significant component of machine learning, facilitating the generation of data that closely resembles real-world samples. Autoregressive models, Gaussian Generative Models, Probabilistic Generative Models, Hidden Markov Models, and Flow-Based Models have each contributed uniquely to the progress of artificial intelligence. As these models continue to evolve and mature, the potential for generative AI to transform various industries and reshape human-machine interactions is bound to increase significantly.