Deep generative models (DGMs) combine generative models and deep neural networks. Deep generative models provide efficient data generation models by using neural networks as the generative models with a low number of parameters than the amount of data to be trained. DGM follows an unsupervised learning approach that analyzes and learn the unlabeled data to discover hidden structure. The significant role of DGM is to train the neural network with many hidden layers and approximate complicated, high dimension probability distributions using a large number of datasets. Generative models are broadly based on cost function and energy. Cost function-based models are autoencoders and generative adversarial networks.
Energy-based models are the boltzmann machine and its variants and deep belief network. Autoencoders and Generative adversarial networks are the most common and efficient deep generative models. Robotics, 3D technology, natural language processing, medical imaging, speech recognition, and generation are various fields where the generative models are applied. Some of the applications tasks of deep generative models are text generation, data retrieval, super-resolution, one-shot generation, data indexing, video generation, data augmentation, cross-modal generation, image editing, and many more. Future advancements in deep generative models include incorporating domain-specific knowledge with deep generative models, deep graph generative models for dynamic graphs, and unified generative models.
• Deep generative models (DGMs) are the hotly researched field in artificial intelligence in recent years and have experienced rapid growth in the business world.
• Deep generative models leverage deep neural networks and generative models trained to approximate complicated, high-dimensional probability distributions using a large number of samples.
• Generative models can generate new samples for a given distribution used for fast data indexing and retrieval and other tasks.
• In short, deep generative models generate a joint distribution of target and training data.
• Even though many recent advances and success in deep generative modeling, DGM training is an ill-posed problem since uniquely identifying a probability distribution from a finite number of samples is impossible.
• In real-world scenarios, deep generative models have achieved unprecedented breakthroughs in solving complicated and modern problems.