Fetching the paper…
Reading the bibliography…
We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models.
On the theory of stochastic processes, with particular reference to applications
William Feller · 1949
Earlier work this paper cites.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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.
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
Earlier work this paper cites.
Memory-efficient backpropagation through time
Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex Graves · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 2017
Earlier work this paper cites.
Towards the automatic anime characters creation with generative adversarial networks
Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, and Zhihao Fang · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Metrics for deep generative models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, and Patrick Smagt · 2018
Cited alongside, same era.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
Cited alongside, same era.
The riemannian geometry of deep generative models
Hang Shao, Abhishek Kumar, and P Thomas Fletcher · 2018
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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
Later among the works it cites.
Closed-form factorization of latent semantics in gans
Yujun Shen and Bolei Zhou · 2021
Later among the works it cites.
Latentclr: A contrastive learning approach for unsupervised discovery of interpretable directions
Oğuz Kaan Yüksel, Enis Simsar, Ezgi Gülperi Er, and Pinar Yanardag · 2021
Later among the works it cites.
Perception prioritized training of diffusion models
Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, and Sungroh Yoon · 2022
Later among the works it cites.
Stylegan-nada: Clip-guided domain adaptation of image generators
Rinon Gal, Or Patashnik, Haggai Maron, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Georgios Arvanitidis, Søren Hauberg, and Bernhard Schölkopf · 2020
Cited alongside, same era.
Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Unsupervised discovery of interpretable directions in the gan latent space
Andrey Voynov and Artem Babenko · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Diffusion autoencoders: Toward a meaningful and decodable representation
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn · 2021
Cited alongside, same era.
Mingi Kwon, Jaeseok Jeong, and Youngjung Uh · 2022
Later among the works it cites.
Generating high fidelity data from low-density regions using diffusion models
Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, and Cristian Canton · 2022
Later among the works it cites.
Unsupervised representation learning from pre-trained diffusion probabilistic models
Zijian Zhang, Zhou Zhao, and Zhijie Lin · 2022
Later among the works it cites.
Aligning text-to-image models using human feedback
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu · 2023
Closest in time.
Unsupervised discovery of semantic latent directions in diffusion models
Yong-Hyun Park, Mingi Kwon, Junghyo Jo, and Youngjung Uh · 2023
Closest in time.
Chatface: Chat-guided real face editing via diffusion latent space manipulation
Dongxu Yue, Qin Guo, Munan Ning, Jiaxi Cui, Yuesheng Zhu, and Li Yuan · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala · 2023
Closest in time.