Deep Generative Models
Generative models are forms of Artificial Intelligence (AI) and Machine Learning (ML) that use deep neural networks that understand the distribution of complex training data sets. This knowledge facilitates the generation of large datasets that know the probability of the next item in a sequence. Applications include natural language processing, speech processing, and computer vision.
Why are Deep Generative Models Important?
Deep Generative Models are important in cases where you need more plausible or authentic generated data. Because these models are deeply aware of the underlying distribution and probability inherent in the training dataset, they can synthesize similar datasets.
How do Deep Generative Models Work?
To create more authentic output from your generative model, you can use Generative Adversarial Networks (GAN) to create a synthetic training data set that trains a second competing Neural Network. The generated neural network instances become negative training examples for the discriminator. By learning to distinguish the generator’s fake data from actual data, generating more plausible and original new data is possible.
Examples of Deep Learning Algorithms
Different algorithms are applicable depending on the application of a deep generative model. These include the following.
Variational Autoencoders
Variational autoencoders can learn to reconstruct and generate new samples from a provided dataset. By utilizing a latent space, variational autoencoders can represent data continuously and smoothly. This enables the generation of variations of the input data with smooth transitions.
Generative Adversarial Networks
Generative adversarial networks (GANs) are generative models that can create new data instances similar to but not the same as the training data sets. GANs are great for creating images but not as sophisticated as diffusion models.
Autoregressive Models
An autoregressive model is a statistical model used to understand and predict future values in a time series based on past values.
Normalizing Flow Models
Normalizing Flows is a method for constructing complex distributions by transforming a probability density through a series of invertible mappings. By repeatedly applying the rule for change of variables, the initial density ‘flows’ through the sequence of invertible mappings.
Energy-Based Models
An energy-based model is a generative model usually used in statistical physics. After learning the data distribution of a training data set, the generative model can produce other datasets matching the data distributions.
Score-Based Models
Score-based generative models estimate the scores from the training data, allowing the model to navigate the data space according to the learned distribution and generate similar new data.
Applications of Deep Generative Models
Below are some use cases for deep generative models being applied in the real world today:
- Autonomous vehicle systems use inputs from visual and Lidar sensors fed to a neural network that predicts future behavior to make proactive course corrections thousands of times a second.
- Fraud detection compares historical behavior to current transactions to detect anomalies and act accordingly.
- Virtual assistants learn a person’s taste in music, their schedule, purchasing history and any other information they have access to make recommendations. For example, it can provide travel times to home or places to work.
- Entertainment systems can recommend movies based on past viewing of similar content.
- A smartwatch can warn of potential medical conditions, over-exertion, and lack of sleep to oversee the owner’s well-being.
- Images taken with a digital camera or scanned images can be enhanced by increasing sharpness, balancing colors, and suggesting crops.
- Captions can be auto-generated for movies or meeting videos to enhance playback.
- Handwriting style can be learned, and new text can be generated in the same style.
- Captioned videos can have captions generated in multiple languages.
- Photo libraries can be tagged with descriptions to make finding similar ones or duplicates easier.
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Deep generative models are advanced machine learning frameworks that learn the underlying structure of data and generate new, realistic data samples. They use deep neural networks to model complex patterns, making them useful for applications like image synthesis, text generation, and data augmentation.
Deep generative models work by training on large datasets to understand the probability distribution of the data. Once trained, they can produce new examples that resemble the original data. Techniques such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models are commonly used to achieve this.
Deep generative models are important because they enable machines to create new, meaningful data that can be used for research, design, and problem-solving. They enhance creativity, support data synthesis for training other models, and drive innovation in fields such as art, medicine, and natural language processing.
Applications include image and video generation, natural language processing, drug discovery, anomaly detection, and data simulation. They are also used in industries like entertainment, healthcare, and finance to create synthetic data or enhance existing datasets.
The main types include:
- Generative adversarial networks (GANs): Models that use a generator and discriminator in competition to create realistic outputs.
- Variational autoencoders (VAEs): Models that learn latent representations to generate new data points.
- Diffusion models: Models that iteratively transform random noise into structured data.
Actian supports deep generative model development through powerful data integration, management, and analytics capabilities. By providing scalable infrastructure and high-performance data processing, Actian enables organizations to train, test, and deploy generative models efficiently and with confidence.
