Deep learning generative models are neural network architectures designed to learn and generate new data samples that resemble a training dataset. These models have applications in various fields such as image generation, text generation, audio synthesis, and more. Here are some popular deep learning generative models:
Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a competitive setting. The generator aims to generate realistic samples, while the discriminator learns to distinguish between real and generated samples. The training process encourages the generator to produce samples that are indistinguishable from real data. GANs have been used for tasks like image generation, style transfer, and data augmentation.
Variational Autoencoders (VAEs): VAEs are probabilistic generative models that learn to encode and decode data. The model consists of an encoder network that maps input data to a latent space and a decoder network that reconstructs the input data from samples drawn from the latent space. VAEs are trained to maximize the likelihood of generating the input data and minimizing the discrepancy between the learned latent distribution and a prior distribution (typically a Gaussian distribution). VAEs are used for tasks like image generation, anomaly detection, and data imputation.
Autoregressive Models: Autoregressive models generate data by modeling the conditional distribution of each data point given previous data points. Examples of autoregressive models include PixelCNN, WaveNet, and Transformer models. These models are often used for tasks like image generation, text generation, and audio synthesis.
Flow-Based Models: Flow-based models are generative models that learn to transform a simple input distribution (e.g., Gaussian) into a complex data distribution through a series of invertible transformations. Flow-based models guarantee efficient sampling and exact likelihood computation. Examples include RealNVP, Glow, and FFJORD. These models are used for tasks like image generation and density estimation.
Generative Moment Matching Networks (GMMNs): GMMNs are a class of generative models that learn to match the moments of the generated data distribution with those of the real data distribution. They are trained using moment matching objectives and have been applied to tasks like image generation and data augmentation.
These are just a few examples of deep learning generative models, and there are many other variants and architectures tailored to specific tasks and datasets. Generative models have seen significant advancements in recent years and continue to be an active area of research in deep learning.