Learning FRAME Models Using CNN Filters for Knowledge Visualization

Keywords

Computer science, Artificial intelligence, Convolutional neural network, Generative grammar, Generative model

References

• Handbook of Markov Chain Monte CarloSteve Brooks, Andrew Gelman, Galin L. Jones — 2011
• Inducing wavelets into random fields via generative boostingJianwen Xie, Yang Lu, Song‐Chun Zhu — 2016
• Equivalence of Julesz Ensembles and FRAME ModelsYing Wu, Song Chun Zhu, Xiu Wen Liu — 2000
• Very Deep Convolutional Networks for Large-Scale Image RecognitionKaren Simonyan, Andrew Zisserman — 2014
• Deep Convolutional Inverse Graphics NetworkTejas D. Kulkarni, WILLIAM F. WHITNEY, Pushmeet Kohli — 2015
• Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsDanilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra — 2014
• Learning to generate chairs with convolutional neural networksAlexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox — 2014
• A Neural Algorithm of Artistic StyleLeon A. Gatys, Alexander S. Ecker, Matthias Bethge — 2015
• Generative Modeling of Convolutional Neural NetworksJifeng Dai, Yang Lu, Yingnian Wu — 2014
• Auto-Encoding Variational BayesDiederik P. Kingma, Max Welling — 2013
• MatConvNet: Convolutional Neural Networks for MATLABAndrea Vedaldi, Karel Lenc — 2014
• On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity ratesLaurent Younès — 1999
• The "wake-sleep" algorithm for unsupervised neural networks.Geoffrey E. Hinton, Peter Dayan, Brendan J. Frey — 1995
• Learning Sparse FRAME Models for Natural Image PatternsJianwen Xie, Wenze Hu, Song‐Chun Zhu — 2014
• Unsupervised learning of compositional sparse code for natural image representationYi Hong, Zhangzhang Si, Wenze Hu — 2013
• Learning Active Basis Model for Object Detection and RecognitionYing Wu, Zhangzhang Si, Haifeng Gong — 2010
• Unsupervised Discovery of Nonlinear Structure Using Contrastive BackpropagationGeoffrey E. Hinton, Simon Osindero, Max Welling — 2006
• ImageNet: A large-scale hierarchical image databaseJia Deng, Wei Dong, Richard Socher — 2009
• Gradient-based learning applied to document recognitionYann LeCun, Léon Bottou, Yoshua Bengio — 1998
• Training Products of Experts by Minimizing Contrastive DivergenceGeoffrey E. Hinton — 2002

Cited by

• Combining Markov Random Fields and Convolutional Neural Networks for Image SynthesisChuan Li, Michael Wand — 2016
• DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize MoviesAlexander G. Anderson, Cory P. Berg, Daniel P. Mossing — 2016
• Stochastic Image Models from SIFT-Like DescriptorsAgnès Desolneux, Arthur Leclaire — 2018
• Place recognition based visual localization in changing environmentsYongliang Qiao — 2017

Related papers

• Interpretable Convolutional Neural NetworksQuanshi Zhang, Ying Wu, Song‐Chun Zhu — 2017
• B-CNN: Branch Convolutional Neural Network for Hierarchical ClassificationXinqi Zhu, Michael Bain — 2017
• Interpretation of Deep CNN Based on Learning Feature Reconstruction With Feedback WeightsNaeem Ul Islam, Sukhan Lee — 2019
• Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category RecognitionXuesong Zhang, Fei Yan, Yan Zhuang — 2019
• Beam search for learning a deep Convolutional Neural Network of 3D shapesXu Xu, Siniša Todorović — 2016