PDF4PRO ⚡AMP

Modern search engine that looking for books and documents around the web

Example: tourism industry

InfoGAN: Interpretable Representation Learning by ... - NIPS

InfoGAN: Interpretable Representation Learning byInformation maximizing generative adversarial NetsXi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , Pieter Abbeel UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAIAbstractThis paper describes InfoGAN, an information -theoretic extension to the Gener-ative adversarial Network that is able to learn disentangled representations in acompletely unsupervised manner. InfoGAN is a generative adversarial networkthat also maximizes the mutual information between a small subset of the latentvariables and the observation. We derive a lower bound of the mutual informationobjective that can be optimized efficiently. Specifically, InfoGAN successfullydisentangles writing styles from digit shapes on the MNIST dataset, pose fromlighting of 3D rendered images, and background digits from the central digit onthe SVHN dataset. It also discovers visual concepts that include hair styles, pres-ence/absence of eyeglasses, and emotions on the CelebA face dataset.

Information Maximizing Generative Adversarial Nets Xi Chen yz, Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskeverz, Pieter Abbeelyz yUC Berkeley, Department of Electrical Engineering and Computer Sciences zOpenAI Abstract This paper describes InfoGAN, an information-theoretic extension to the Gener-

Fullscreen Download
Loading..

Tags:

  Information, Maximizing, Nets, Adversarial, Generative, Information maximizing generative adversarial nets

Information

Domain:

Source:

Link to this page:

Please notify us if you found a problem with this document:

Spam in document Broken preview Other abuse

Transcription of InfoGAN: Interpretable Representation Learning by ... - NIPS

Documents from same domain

Generative Adversarial Nets - NIPS

papers.nips.cc

Generative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified.

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks, Adversar ial, Adversar

Time-series Generative Adversarial Networks

papers.nips.cc

A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...

  Network, Adversarial, Generative, Generative adversarial networks

On Discriminative vs. Generative Classifiers: A …

papers.nips.cc

On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science Division University of California, Berkeley

  Comparison, Logistics, Regression, Logistic regression

Character-level Convolutional Networks for Text Classification

papers.nips.cc

Applying convolutional networks to text classification or natural language processing at large was explored in literature. It has been shown that ConvNets can be directly applied to distributed [6] [16] or discrete [13] embedding of words, without any knowledge on the syntactic or semantic structures of a language.

  Network, Convolutional, Convolutional networks, Semantics

SAGA: A Fast Incremental Gradient Method With Support for ...

papers.nips.cc

SAGA is preferred over SVRG both theoretically and in practice. For neural networks, where no theory is available for either method, the storage of gradients is generally more expensive than the

  With, Methods, Support, Fast, Saga, Derating, Incremental, A fast incremental gradient method with support

Thinking Fast and Slow with Deep Learning and Tree Search

papers.nips.cc

System 1 is a fast, unconscious and automatic mode of thought, also known as intuition or heuristic process. System 2, an evolutionarily recent process unique to humans, is a slow, conscious, explicit

  With, Learning, Search, Tree, Thinking, Deep, Fast, Slow, Thinking fast and slow with deep learning and tree search

A Growing Neural Gas Network Learns Topologies

papers.nips.cc

A Growing Neural Gas Network Learns Topologies 627 a) Delaunay triangulation b) induced Delaunay triangulation Figure 1: Two ways of defining closeness among a set of points.

  Network, Neural, Topologies, Neural gas network

Attention is All you Need - Neural Information Processing ...

papers.nips.cc

Attention Is All You Need Ashish Vaswani Google Brain avaswani@google.com Noam Shazeer Google Brain noam@google.com Niki Parmar Google Research nikip@google.com

  Needs, Attention, Neural, Attention is all you need

ImageNet Classification with Deep Convolutional Neural ...

papers.nips.cc

Challenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images. ILSVRC-2010 is the only version ...

  Challenges, Scale, Visual, Recognition, Ilsvrc, Scale visual recognition challenge

Hidden Technical Debt in Machine Learning Systems

papers.nips.cc

account for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns. 1 Introduction As the machine learning (ML) community continues to accumulate years of experience with live

  System, Design, Machine, Technical, Learning, Debt, Hidden, Hidden technical debt in machine learning systems

Related documents

arXiv:1411.1784v1 [cs.LG] 6 Nov 2014

arxiv.org

Generative adversarial nets were recently introduced as a novel way to train a generative model. They consists of two ‘adversarial’ models: a generative model Gthat captures the data distribution, and a discriminative model Dthat estimates the probability that a …

  Nets, Adversarial generative nets, Adversarial, Generative

生成对抗网络及其在图像生成中的 ... - ict.ac.cn

cjc.ict.ac.cn

and computational challenges. At the same time, generative adversarial networks are the latest and most successful technology among generative models. Especially in terms of image generation, compared with other generation models, generative adversarial networks can not only avoid complicated calculations, but also generate better quality images.

  Adversarial, Generative, Generative adversarial

Related search queries

Generative adversarial nets, Generative, Adversarial, Generative adversarial