Fetching the paper…
Reading the bibliography…
The excellent generative capabilities of text-to-image diffusion models suggest they learn informative representations of image-text data.
A sequential procedure for selecting the population with the largest mean from k normal populations
Edward Paulson · 1964
Earlier work this paper cites.
The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Ng and Michael Jordan · 2001
Earlier work this paper cites.
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
Eyal Even-Dar, Shie Mannor, and Y. Mansour · 2002
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou · 2010
Earlier work this paper cites.
Auto-encoding Variational Bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou · 2017
Earlier work this paper cites.
CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Earlier work this paper cites.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
Earlier work this paper cites.
LXMERT: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors
Ryan Burgert, Kanchana Ranasinghe, Xiang Li, and Michael S Ryoo · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Later among the works it cites.
Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
Later among the works it cites.
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
Martha Lewis, Qinan Yu, Jack Merullo, and Ellie Pavlick · 2022
Later among the works it cites.
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unleashing Text-to-Image Diffusion Models for Visual Perception
Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie Zhou, and Jiwen Lu · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
CMA-CLIP: Cross-modality attention clip for image-text classification
Huidong Liu, Shaoyuan Xu, Jinmiao Fu, Yang Liu, Ning Xie, Chien-Chih Wang, Bryan Wang, and Yi Sun · 2021
Cited alongside, same era.
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
Later among the works it cites.
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
Sanjay Subramanian, Will Merrill, Trevor Darrell, Matt Gardner, Sameer Singh, and Anna Rohrbach · 2022
Later among the works it cites.
Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al · 2022
Later among the works it cites.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
Later among the works it cites.
Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al · 2023
Closest in time.
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, P. Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang · 2023
Closest in time.
Understanding the Diffusion Objective as a Weighted Integral of ELBOs
Diederik P Kingma and Ruiqi Gao · 2023
Closest in time.
Your Diffusion Model is Secretly a Zero-Shot Classifier
Alexander C Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
Closest in time.