Stability and Expressiveness of Deep Generative Models PDF Download
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Author: Lars Morton Mescheder
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Lars Morton Mescheder
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Lars Morton Mescheder
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Jakub M. Tomczak
Publisher: Springer Nature
ISBN: 303164087X
Category :
Languages : en
Pages : 325
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Book Description
Author: Jakub M. Tomczak
Publisher: Springer Nature
ISBN: 3030931587
Category : Computers
Languages : en
Pages : 210
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Book Description
This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.
Author: David Foster
Publisher: "O'Reilly Media, Inc."
ISBN: 109813415X
Category : Computers
Languages : en
Pages : 456
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Book Description
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
Author: Zhifeng Kong
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Deep Generative Models are a kind of unsupervised deep learning methods that learn the data distribution from samples and then generate unseen, high-quality samples from the learned distributions. These models have achieved tremendous success in different domains and tasks. However, many questions are not well-understood for these models. In order to better understand these models, in this thesis, we investigate the following questions: (i) what is the representation power of deep generative models, and (ii) how to identify and mitigate trustworthy concerns in deep generative models. We study the representation power of deep generative models by looking at which distributions they can approximate arbitrarily well. we study normalizing flows and rigorously establish bounds on their expressive power. Our results indicate that some basic flows are highly expressive in one dimension, but in higher dimensions their representation power may be limited, especially when the flows have moderate depth. We then prove residual flows are universal approximators in maximum mean discrepancy and provide upper bounds on the depths under different assumptions. We next investigate three different trustworthy concerns. The first is how to explain the black box neural networks in these models. We introduce VAE-TracIn, a computationally efficient and theoretically sound interpretability solution, for VAEs. We evaluate VAE-TracIn on real world datasets with extensive quantitative and qualitative analysis. The second is how to mitigate privacy issues in learned generative models. We propose a density-ratio-based framework for efficient approximate data deletion in generative models, which avoids expensive re-training. We provide theoretical guarantees under various learner assumptions and empirically demonstrate our methods across a variety of generative methods. The third is how to prevent undesirable outputs from deep generative models. We take a compute-friendly approach and investigate how to post-edit a pre-trained model to redact certain samples. We consider several unconditional and conditional generative models and various types of descriptions of redacted samples. Extensive evaluations on real-world datasets show our algorithms outperform baseline methods in redaction quality as well as robustness while retaining high generation quality.
Author: Christian Bauckhage
Publisher: Springer Nature
ISBN: 3030926591
Category : Computers
Languages : en
Pages : 734
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Book Description
This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic. The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.
Author: Sharon Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Evaluation drives and tracks progress in every field. Metrics of evaluation are designed to assess important criteria in an area, and aid us in understanding the quantitative differences between one breakthrough and another. In machine learning, evaluation metrics have historically acted as north stars towards which researchers have optimized and organized their methods and findings. While evaluation metrics have been straightforward to construct and implement in some subfields of machine learning, they have been notoriously difficult to design in generative models. Several reasons emerge to explain this: (1) there are no gold standard outputs to compare against, unlike held-out test sets, (2) because of their diverse training methods and formulations, inherent model properties are difficult to measure consistently, and sampled outputs are often used for evaluation instead, (3) dependence on external (pretrained) models that add another layer of bias and uncertainty, and (4) inconsistent results without a large number of samples. As a result, generative models have suffered from noisy assessments that occupy a changing evaluation landscape, in contrast to the relative stability of their discriminative counterparts. In this manuscript, we examine several important criteria for generative models and introduce evaluation metrics to address each one while discussing the aforementioned issues in generative model evaluation. In particular, we examine the challenge of measuring the perceptual realism of generated outputs and introduce a human-in-the-loop evaluation system that leverages psychophysics theory to ground the method in human perception literature and crowdsourcing techniques to construct an efficient, reliable, and consistent method for comparing different models. In addition to this, we analyze disentanglement, an increasingly important property for assessing learned representations, by measuring an intrinsic property of a generative model's data manifold using persistent homology. The final work in this manuscript takes a step towards assessing a generative model and its different modes with a key application in mind, specifically the stylistic fidelity across different generated modes in a multimodal setting.
Author: David Foster
Publisher: O'Reilly Media
ISBN: 1492041912
Category : Computers
Languages : en
Pages : 330
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Book Description
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Author: Sandy Engelhardt
Publisher: Springer Nature
ISBN: 3030882101
Category : Computers
Languages : en
Pages : 278
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Book Description
This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems.
