Fetching the paper…
Reading the bibliography…
Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, and AI for science.
An empirical bayes estimator of the mean of a normal population
Koichi Miyasawa et al · 1961
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
Earlier work this paper cites.
Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
Earlier work this paper cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Earlier work this paper cites.
High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
Earlier work this paper cites.
Bisenet: Bilateral segmentation network for real-time semantic segmentation
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang · 2018
Earlier work this paper cites.
Revisiting the polyak step size
Elad Hazan and Sham Kakade · 2019
Earlier work this paper cites.
Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Earlier work this paper cites.
Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Fusedream: Training-free text-to-image generation with improved clip+ gan space optimization
Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, and Qiang Liu · 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.
Prompt-to-prompt image editing with cross attention control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2022
Cited alongside, same era.
Manifold preserving guided diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J Zico Kolter, Ruslan Salakhutdinov, et al · 2023
Later among the works it cites.
Adaptdiffuser: Diffusion models as adaptive self-evolving planners
Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, and Ping Luo · 2023
Later among the works it cites.
More control for free! image synthesis with semantic diffusion guidance
Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, and Trevor Darrell · 2023
Later among the works it cites.
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine · 2022
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Adversarial open domain adaptation for sketch-to-photo synthesis
Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen, and Jan P Allebach · 2022
Cited alongside, same era.
Sicheng Mo, Fangzhou Mu, Kuan Heng Lin, Yanli Liu, Bochen Guan, Yin Li, and Bolei Zhou · 2023
Later among the works it cites.
Loss-guided diffusion models for plug-and-play controllable generation
Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, and Arash Vahdat · 2023
Later among the works it cites.
Plug-and-play diffusion features for text-driven image-to-image translation
Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel · 2023
Later among the works it cites.
Toward open-ended embodied tasks solving
Wei Wang, Dongqi Han, Xufang Luo, Yifei Shen, Charles Ling, Boyu Wang, and Dongsheng Li · 2023
Later among the works it cites.
Freedom: Training-free energy-guided conditional diffusion model
Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Later among the works it cites.
Towards predicting equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, et al · 2023
Later among the works it cites.
Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Homes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
Closest in time.
Diffusion self-guidance for controllable image generation
Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski · 2024
Closest in time.
Mass-producing failures of multimodal systems with language models
Shengbang Tong, Erik Jones, and Jacob Steinhardt · 2024
Closest in time.
Restart sampling for improving generative processes
Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, and Tommi Jaakkola · 2024
Closest in time.