Search results with tag "Generative models"
Perceptual Generative Adversarial Networks for Small ...
openaccess.thecvf.com2.2. Generative Adversarial Networks The Generative Adversarial Networks (GANs) [14]is a framework for learning generative models. Mathieu et al. [26] and Dentonet al. [6] adopted GANs for the appli-cation of image generation. In [22] and [40], GANs were employed to learn a mapping from one manifold to another
Machine Learning: Generative and Discriminative Models
cedar.buffalo.eduprobabilistic generative models • Example: Autonomous agents in AI – ELIZA : natural language rules to emulate therapy session – Manual specification of models, theories are increasingly difficult • Greater availability of data and computational power to …
Unsupervised Anomaly Detection with Generative Adversarial ...
arxiv.orgGANs enable to learn generative models generating detailed realistic im-ages [9,10,11]. Radford et al. [12] introduced deep convolutional generative ad-versarial networks (DCGANs) and showed that GANs are capable of capturing semantic image content enabling vector arithmetic for visual concepts. Yeh et
GIRAFFE: Representing Scenes As Compositional Generative ...
openaccess.thecvf.comgenerative models operate in 2D, we incorporate a compo-sitional 3D scene representation into the generative model. This leads to more consistent image synthesis results, e.g. note how, in contrast to our method, translating one object might change the other when operating in 2D (Fig. 2a and 2b). It further allows us to perform complex ...
Jukebox: A Generative Model for Music - arXiv
arxiv.orgThe field of generative models has made tremendous progress in the last few years. One of the aims of gen-erative modeling is to capture the salient aspects of the data and to generate new instances indistinguishable from the true data The hypothesis is that by learning to produce the data we can learn the best features of the data1. We are
Lecture 13: Generative Models
cs231n.stanford.eduFully visible belief network Use chain rule to decompose likelihood of an image x into product of 1-d distributions: Explicit density model Likelihood of image x Probability of i’th pixel value given all previous pixels Will need to define ordering of “previous pixels” Complex distribution over pixel values => Express using a neural network!