"Wasserstein GAN"
Written by James Allingham: http://www.depthfirstlearning.com/2019/WassersteinGAN
#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks
Written by James Allingham: http://www.depthfirstlearning.com/2019/WassersteinGAN
#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks
Discrete Flows: Invertible Generative Models of Discrete Data
Tran et al.: https://openreview.net/forum?id=rJlo4UIt_E
#ArtificialIntelligence #DeepLearning #GenerativeModels
Tran et al.: https://openreview.net/forum?id=rJlo4UIt_E
#ArtificialIntelligence #DeepLearning #GenerativeModels
openreview.net
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show...
Understanding Neural Networks via Feature Visualization: A survey
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
"Wasserstein GAN"
Written by James Allingham: http://www.depthfirstlearning.com/2019/WassersteinGAN
#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks
Written by James Allingham: http://www.depthfirstlearning.com/2019/WassersteinGAN
#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks
Understanding Neural Networks via Feature Visualization: A survey
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels
GRAM: Scalable Generative Models for Graphs with Graph Attention Mechanism
Kawai et al.: https://arxiv.org/abs/1906.01861
#ArtificialIntelligence #GenerativeModels #MachineLearning
Kawai et al.: https://arxiv.org/abs/1906.01861
#ArtificialIntelligence #GenerativeModels #MachineLearning
arXiv.org
Scalable Generative Models for Graphs with Graph Attention Mechanism
Graphs are ubiquitous real-world data structures, and generative models that
approximate distributions over graphs and derive new samples from them have
significant importance. Among the known...
approximate distributions over graphs and derive new samples from them have
significant importance. Among the known...
Residual Flows for Invertible Generative Modeling
Chen et al.: https://arxiv.org/abs/1906.02735
#artificialintelligence #deeplearning #generativemodels
Chen et al.: https://arxiv.org/abs/1906.02735
#artificialintelligence #deeplearning #generativemodels
"Variational Autoencoders and Nonlinear ICA: A Unifying Framework"
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
arXiv.org
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often,...
Lifelong GAN: Continual Learning for Conditional Image Generation
Zhai et al.: https://arxiv.org/abs/1907.10107
#deeplearning #generativemodels #GAN
Zhai et al.: https://arxiv.org/abs/1907.10107
#deeplearning #generativemodels #GAN
Lifelong GAN: Continual Learning for Conditional Image Generation
Zhai et al.: https://arxiv.org/abs/1907.10107
#deeplearning #generativemodels #GAN
Zhai et al.: https://arxiv.org/abs/1907.10107
#deeplearning #generativemodels #GAN
Tensorflow implementation of U-GAT-IT
Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
GitHub, by Junho Kim : https://github.com/taki0112/UGATIT
#tensorflow #unsupervisedlearning #generativemodels
Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
GitHub, by Junho Kim : https://github.com/taki0112/UGATIT
#tensorflow #unsupervisedlearning #generativemodels
GitHub
GitHub - taki0112/UGATIT: Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive…
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020) - taki0112/UGATIT
Implicit Generation and Generalization in Energy-Based Models
Yilun Du and Igor Mordatch : https://arxiv.org/abs/1903.08689
#EnergyBasedModels #MachineLearning #GenerativeModels
Yilun Du and Igor Mordatch : https://arxiv.org/abs/1903.08689
#EnergyBasedModels #MachineLearning #GenerativeModels
arXiv.org
Implicit Generation and Generalization in Energy-Based Models
Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based...
Tensorflow implementation of U-GAT-IT
Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
GitHub, by Junho Kim : https://github.com/taki0112/UGATIT
#tensorflow #unsupervisedlearning #generativemodels
Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
GitHub, by Junho Kim : https://github.com/taki0112/UGATIT
#tensorflow #unsupervisedlearning #generativemodels
GitHub
GitHub - taki0112/UGATIT: Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive…
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020) - taki0112/UGATIT
"A tutorial on energy-based learning"
Yann LeCun, Sumit Chopra, and Raia Hadsell (2006) : http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf
#EnergyBasedModels #GenerativeModels #GraphTransformerNetworks
Yann LeCun, Sumit Chopra, and Raia Hadsell (2006) : http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf
#EnergyBasedModels #GenerativeModels #GraphTransformerNetworks
Unsupervised Doodling and Painting with Improved SPIRAL
Mellor et al. : https://arxiv.org/pdf/1910.01007.pdf
Blog : https://learning-to-paint.github.io
#ReinforcementLearning #GenerativeModels #DeepLearning
Mellor et al. : https://arxiv.org/pdf/1910.01007.pdf
Blog : https://learning-to-paint.github.io
#ReinforcementLearning #GenerativeModels #DeepLearning
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
arXiv.org
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often,...
GPT-2 and the Nature of Intelligence
Gary Marcus, The Gradient: https://thegradient.pub/gpt2-and-the-nature-of-intelligence/
#GPT2 #DeepLearning #GenerativeModels
Gary Marcus, The Gradient: https://thegradient.pub/gpt2-and-the-nature-of-intelligence/
#GPT2 #DeepLearning #GenerativeModels
The Gradient
GPT-2 and the Nature of Intelligence
Anything that looks like genuine understanding is just an illusion.