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Diffusion Denoising Probability Models (DDPM) and Vision Transformer (ViT) have demonstrated significant progress in generative tasks and discriminative tasks, respectively, and thus far these models have largely been developed in their own domains.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel · 1989
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Classification with hybrid generative/discriminative models
Rajat Raina, Yirong Shen, Andrew Mccallum, and Andrew Y Ng · 2004
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Semi-supervised learning with deep generative models
Danilo Jimenez Rezende Diederik Kingma, Shakir Mohamed and Max Welling · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Triple generative adversarial nets
LI Chongxuan, Taufik Xu, Jun Zhu, and Bo Zhang · 2017
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A downsampled variant of imagenet as an alternative to the cifar datasets, 2017
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
Cited alongside, same era.
Adaptive input representations for neural language modeling
Alexei Baevski and Michael Auli · 2019
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Implicit generation and generalization in energy-based models
Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
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Rethinking attention with performers
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, and Adrian Weller · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Improved Contrastive Divergence Training of Energy Based Models
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2021
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Yilun Du and Igor Mordatch · 2019
Cited alongside, same era.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2019
Cited alongside, same era.
Hybrid models with deep and invertible features
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Learning deep transformer models for machine translation
Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P. Kingma · 2021
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No mcmc for me: Amortized sampling for fast and stable training of energy-based models
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, and David Duvenaud · 2021
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Transgan: Two pure transformers can make one strong gan, and that can scale up
Yifan Jiang, Shiyu Chang, and Zhangyang Wang · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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Vision transformer for small-size datasets, 2021
Seung Hoon Lee, Seunghyun Lee, and Byung Cheol Song · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
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VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat · 2021
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JEM++: Improved Techniques for Training JEM
Xiulong Yang and Shihao Ji · 2021
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Analytic-DPM: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
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An Empirical Study of Training End-to-End Vision-and-Language Transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng · 2022
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Conditional gans with auxiliary discriminative classifier
Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, and Xueqi Cheng · 2022
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Vitgan: Training gans with vision transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, and Ce Liu · 2022
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Mcmc should mix: Learning energy-based model with neural transport latent space mcmc
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, and Ying Nian Wu · 2022
Closest in time.