An Introduction to Deep Generative Modeling

• URL: http://arxiv.org/abs/2103.05180v1
• Date: Tue, 9 Mar 2021 02:19:06 GMT
• Title: An Introduction to Deep Generative Modeling
• Authors: Lars Ruthotto and Eldad Haber
• Abstract summary: Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions. We provide an introduction to DGMs and a framework for modeling the three most popular approaches. Our goal is to enable and motivate the reader to contribute to this proliferating research area.
• Score: 8.909115457491522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions using a large number of samples. When trained successfully, we can use the DGMs to estimate the likelihood of each observation and to create new samples from the underlying distribution. Developing DGMs has become one of the most hotly researched fields in artificial intelligence in recent years. The literature on DGMs has become vast and is growing rapidly. Some advances have even reached the public sphere, for example, the recent successes in generating realistic-looking images, voices, or movies; so-called deep fakes. Despite these successes, several mathematical and practical issues limit the broader use of DGMs: given a specific dataset, it remains challenging to design and train a DGM and even more challenging to find out why a particular model is or is not effective. To help advance the theoretical understanding of DGMs, we provide an introduction to DGMs and provide a concise mathematical framework for modeling the three most popular approaches: normalizing flows (NF), variational autoencoders (VAE), and generative adversarial networks (GAN). We illustrate the advantages and disadvantages of these basic approaches using numerical experiments. Our goal is to enable and motivate the reader to contribute to this proliferating research area. Our presentation also emphasizes relations between generative modeling and optimal transport.

Related papers

• Specialized Foundation Models Struggle to Beat Supervised Baselines [60.23386520331143]
  We look at three modalities -- genomics, satellite imaging, and time series -- with multiple recent FMs and compare them to a standard supervised learning workflow. We find that it is consistently possible to train simple supervised models that match or even outperform the latest foundation models.
  arXiv  Detail & Related papers  (2024-11-05T04:10:59Z)
• A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research [21.66278922813198]
  Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields. This paper offers a comprehensive introduction and tutorial on DGMs, with a focus on their applications in transportation. It begins with an overview of generative models, followed by detailed explanations of fundamental models, a systematic review of the literature, and practical tutorial code to aid implementation.
  arXiv  Detail & Related papers  (2024-10-09T17:11:22Z)
• Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations [52.11801730860999]
  In recent years, the robot learning community has shown increasing interest in using deep generative models to capture the complexity of large datasets. We present the different types of models that the community has explored, such as energy-based models, diffusion models, action value maps, or generative adversarial networks. We also present the different types of applications in which deep generative models have been used, from grasp generation to trajectory generation or cost learning.
  arXiv  Detail & Related papers  (2024-08-08T11:34:31Z)
• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections [15.191007332508198]
  We show that numerical instability of likelihoods in high ambient dimensions is unavoidable when modelling data with low intrinsic dimension. We then show that DGMs on learned representations of autoencoders can be interpreted as approximately minimizing Wasserstein distance.
  arXiv  Detail & Related papers  (2024-04-03T18:00:00Z)
• DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control [68.14798033899955]
  Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability to generate creative content. However, are they usable as large-scale data generators, e.g., to improve tasks in the perception stack, like semantic segmentation? We investigate this question in the context of autonomous driving, and answer it with a resounding "yes"
  arXiv  Detail & Related papers  (2023-12-05T18:34:12Z)
• Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study [71.8330148641267]
  Deep generation models (DGMs) have been experiencing explosive growth from 2022. In this article, we explore the applications of DGMs in improving the efficiency of wireless network management.
  arXiv  Detail & Related papers  (2023-03-30T02:59:51Z)
• When Neural Networks Fail to Generalize? A Model Sensitivity Perspective [82.36758565781153]
  Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenario, namely Single Domain Generalization (Single-DG) We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as "model sensitivity" We propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies.
  arXiv  Detail & Related papers  (2022-12-01T20:15:15Z)
• The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks [4.369506407912208]
  We describe novel training-time attacks resulting in corrupted Deep Generative Models (DGMs) Our attacks are based on adversarial loss functions that combine the dual objectives of attack stealth and fidelity. Our experiments show that - even for large-scale industry-grade DGMs - our attack can be mounted with only modest computational efforts.
  arXiv  Detail & Related papers  (2021-08-03T17:33:38Z)
• Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments [9.023847175654602]
  Generative Adversarial Network (GAN) is an effective method to produce samples of large-scale data distribution. GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously.
  arXiv  Detail & Related papers  (2020-05-27T05:56:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.