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Generative modeling in machine learning aims to synthesize new data samples that are statistically similar to those observed during training.
Generalizing the singular value decomposition
Charles F Van Loan · 1976
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A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
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The haar-wavelet transform in digital image processing: its status and achievements
Piotr Porwik and Agnieszka Lisowska · 2004
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Topic transition detection using hierarchical hidden markov and semi-markov models
Dinh Q Phung, Thi V Duong, Svetha Venkatesh, and Hung H Bui · 2005
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Image augmentations for gan training
Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang · 2006
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Face photo-sketch synthesis and recognition
Xiaogang Wang and Xiaoou Tang · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Gaussian mixture models
Douglas A Reynolds et al · 2009
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Optimal transport: old and new , volume 338
Cédric Villani et al · 2009
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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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 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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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 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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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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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Model-agnostic meta-learning for fast adaptation of deep network
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 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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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Few-shot generative modeling with generative matching networks
Sergey Bartunov and Dmitry Vetrov · 2018
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Demystifying mmd gans
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Implicit maximum likelihood estimation
Ke Li and Jitendra Malik · 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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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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A survey on deep learning: Algorithms, techniques, and applications
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and Sundaraja S Iyengar · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
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Transferring gans: Generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz, Joost Van de Weijer, Abel Gonzalez-Garcia, and Bogdan Raducanu · 2018
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Autoencoder and its various variants
Junhai Zhai, Sufang Zhang, Junfen Chen, and Qiang He · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Brecahad: A dataset for breast cancer histopathological annotation and diagnosis
Alper Aksac, Douglas J Demetrick, Tansel Ozyer, and Reda Alhajj · 2019
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Figr: Few-shot image generation with reptile
Louis Clouâtre and Marc Demers · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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An introduction to variational autoencoders
Diederik P Kingma, Max Welling, et al · 2019
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Mode seeking generative adversarial networks for diverse image synthesis
Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, and Ming-Hsuan Yang · 2019
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Image generation from small datasets via batch statistics adaptation
Atsuhiro Noguchi and Tatsuya Harada · 2019
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Singan: Learning a generative model from a single natural image
Tamar Rott Shaham, Tali Dekel, and Tomer Michaeli · 2019
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Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2019
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Panet: Few-shot image semantic segmentation with prototype alignment
Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng · 2019
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The face of art: landmark detection and geometric style in portraits
Jordan Yaniv, Yael Newman, and Ariel Shamir · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Gan memory with no forgetting
Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, and Lawrence Carin · 2020
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Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions
Ricard Durall, Margret Keuper, and Janis Keuper · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Few-shot image generation with elastic weight consolidation
Yijun Li, Richard Zhang, Jingwan Lu, and Eli Shechtman · 2020
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Dawson: A domain adaptive few shot generation framework
Weixin Liang, Zixuan Liu, and Can Liu · 2020
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Freeze the discriminator: a simple baseline for fine-tuning gans
Sangwoo Mo, Minsu Cho, and Jinwoo Shin · 2020
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Few-shot adaptation of generative adversarial networks
Esther Robb, Wen-Sheng Chu, Abhishek Kumar, and Jia-Bin Huang · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Consistency regularization for generative adversarial networks
Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee · 2020
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Revisit multimodal meta-learning through the lens of multi-task learning
Milad Abdollahzadeh, Touba Malekzadeh, and Ngai-Man Man Cheung · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Instance-conditioned gan
Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michal Drozdzal, and Adriana Romero Soriano · 2021
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A closer look at fourier spectrum discrepancies for cnn-generated images detection
Keshigeyan Chandrasegaran, Ngoc-Trung Tran, and Ngai-Man Cheung · 2021
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Topic modeling using latent dirichlet allocation: A survey
Uttam Chauhan and Apurva Shah · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
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Lofgan: Fusing local representations for few-shot image generation
Zheng Gu, Wenbin Li, Jing Huo, Lei Wang, and Yang Gao · 2021
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Recurrent singan: Towards scale-agnostic single image gans
Xiaoyu He and Zhenyong Fu · 2021
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Improved techniques for training single-image gans
Tobias Hinz, Matthew Fisher, Oliver Wang, and Stefan Wermter · 2021
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2021
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Self-supervised gans with label augmentation
Liang Hou, Huawei Shen, Qi Cao, and Xueqi Cheng · 2021
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Few shot image generation via implicit autoencoding of support sets
Andy Huang, Kuan-Chieh Wang, Guillaume Rabusseau, and Alireza Makhzani · 2021
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A survey on generative adversarial networks: Variants, applications, and training
Abdul Jabbar, Xi Li, and Bourahla Omar · 2021
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Deceive d: Adaptive pseudo augmentation for gan training with limited data
Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy · 2021
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c 3 c^{3} : Contrastive learning for cross-domain correspondence in few-shot image generation
Hyuk-Gi Lee, Gi-Cheon Kang, Changhoon Jeong, Han-Wool Sul, and Byoung-Tak Zhang · 2021
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Towards faster and stabilized gan training for high-fidelity few-shot image synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal · 2021
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The creation and detection of deepfakes: A survey
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Clip-sculptor: Zero-shot generation of high-fidelity and diverse shapes from natural language
Aditya Sanghi, Rao Fu, Vivian Liu, Karl DD Willis, Hooman Shayani, Amir H Khasahmadi, Srinath Sridhar, and Daniel Ritchie · 2023
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Re-gan: Data-efficient gans training via architectural reconfiguration
Divya Saxena, Jiannong Cao, Jiahao Xu, and Tarun Kulshrestha · 2023
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Lfs-gan: Lifelong few-shot image generation
Juwon Seo, Ji-Su Kang, and Gyeong-Moon Park · 2023
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Visual prompt tuning for generative transfer learning
Kihyuk Sohn, Huiwen Chang, José Lezama, Luisa Polania, Han Zhang, Yuan Hao, Irfan Essa, and Lu Jiang · 2023
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Yisroel Mirsky and Wenke Lee · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Few-shot image generation via cross-domain correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang · 2021
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Fast adaptive meta-learning for few-shot image generation
Aniwat Phaphuangwittayakul, Yi Guo, and Fangli Ying · 2021
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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
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
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A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
Yisheng Song, Ting Wang, Puyu Cai, Subrota K Mondal, and Jyoti Prakash Sahoo · 2023
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Smoothness similarity regularization for few-shot gan adaptation
Vadim Sushko, Ruyu Wang, and Juergen Gall · 2023
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Fair generative models via transfer learning
Christopher TH Teo, Milad Abdollahzadeh, and Ngai-Man Cheung · 2023
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Target-aware generative augmentations for single-shot adaptation
Kowshik Thopalli, Rakshith Subramanyam, Pavan Turaga, and Jayaraman J Thiagarajan · 2023
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Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo · 2023
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Tree-ring watermarks: Fingerprints for diffusion images that are invisible and robust
Yuxin Wen, John Kirchenbauer, Jonas Geiping, and Tom Goldstein · 2023
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Data collection and quality challenges in deep learning: A data-centric ai perspective
Steven Euijong Whang, Yuji Roh, Hwanjun Song, and Jae-Gil Lee · 2023
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D3t-gan: Data-dependent domain transfer gans for image generation with limited data
Xintian Wu, Huanyu Wang, Yiming Wu, and Xi Li · 2023
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Multi-national covid-19 ct image-label pairs synthesis via few-shot gans adaptation
Jing Zhang, Yingpeng Xie, Dandan Sun, Ruidong Huang, Tianfu Wang, Baiying Lei, and Kuntao Chen · 2023
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Where is my spot? few-shot image generation via latent subspace optimization
Chenxi Zheng, Bangzhen Liu, Huaidong Zhang, Xuemiao Xu, and Shengfeng He · 2023
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Eqgan: Feature equalization fusion for few-shot image generation
Yingbo Zhou, Zhihao Yue, Yutong Ye, Pengyu Zhang, Xian Wei, and Mingsong Chen · 2023
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Jingyuan Zhu, Huimin Ma, Jiansheng Chen, and Jian Yuan · 2023
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Sagan: Skip attention generative adversarial networks for few-shot image generation
Ali Aldhubri, Jianfeng Lu, and Guanyiman Fu · 2024
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Hypergan-clip: A unified framework for domain adaptation, image synthesis and manipulation
Abdul Basit Anees, Ahmet Canberk Baykal, Muhammed Burak Kizil, Duygu Ceylan, Erkut Erdem, and Aykut Erdem · 2024
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Palp: prompt aligned personalization of text-to-image models
Moab Arar, Andrey Voynov, Amir Hertz, Omri Avrahami, Shlomi Fruchter, Yael Pritch, Daniel Cohen-Or, and Ariel Shamir · 2024
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Renaissance: A survey into ai text-to-image generation in the era of large model
Fengxiang Bie, Yibo Yang, Zhongzhu Zhou, Adam Ghanem, Minjia Zhang, Zhewei Yao, Xiaoxia Wu, Connor Holmes, Pareesa Golnari, David A Clifton, et al · 2024
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Decoupled textual embeddings for customized image generation
Yufei Cai, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Hu Han, and Wangmeng Zuo · 2024
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Improving subject-driven image synthesis with subject-agnostic guidance
Kelvin C. K. Chan, Yang Zhao, Xuhui Jia, Ming-Hsuan Yang, and Huisheng Wang · 2024
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Frequency-auxiliary one-shot domain adaptation of generative adversarial networks
Kan Cheng, Haidong Liu, Jiayu Liu, Bo Xu, and Xinyue Liu · 2024
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P2d: Plug and play discriminator for accelerating gan frameworks
Min Jin Chong, Krishna Kumar Singh, Yijun Li, Jingwan Lu, and David Forsyth · 2024
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Idadapter: Learning mixed features for tuning-free personalization of text-to-image models
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Frequency masking for universal deepfake detection
Chandler Timm Doloriel and Ngai-Man Cheung · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
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Discrete flow matching
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Meta-learning approaches for few-shot learning: A survey of recent advances
Hassan Gharoun, Fereshteh Momenifar, Fang Chen, and Amir H Gandomi · 2024
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Few-shot image generation with reverse contrastive learning
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Conditional distribution modelling for few-shot image synthesis with diffusion models
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Few-shot image generation via style adaptation and content preservation
Xiaosheng He, Fan Yang, Fayao Liu, and Guosheng Lin · 2024
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Augmentation-aware self-supervision for data-efficient gan training
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Magicapture: High-resolution multi-concept portrait customization
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Few-shot adaptation of gans using self-supervised consistency regularization
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Arnab Kumar Mondal, Piyush Tiwary, Parag Singla, and Prathosh A.P · 2024
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Adobe to bring full AI image generation to Photoshop this year
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Chain: Enhancing generalization in data-efficient gans via lipschitz continuity constrained normalization
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Cross initialization for face personalization of text-to-image models
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Orthogonal adaptation for modular customization of diffusion models
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Facechain-sude: Building derived class to inherit category attributes for one-shot subject-driven generation
Pengchong Qiao, Lei Shang, Chang Liu, Baigui Sun, Xiangyang Ji, and Jie Chen · 2024
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Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Wei Wei, Tingbo Hou, Yael Pritch, Neal Wadhwa, Michael Rubinstein, and Kfir Aberman · 2024
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Rg-gan: Dynamic regenerative pruning for data-efficient generative adversarial networks
Divya Saxena, Jiannong Cao, Jiahao Xu, and Tarun Kulshrestha · 2024
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Caduceus: Bi-directional equivariant long-range dna sequence modeling
Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, and Volodymyr Kuleshov · 2024
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Create your world: Lifelong text-to-image diffusion
Gan Sun, Wenqi Liang, Jiahua Dong, Jun Li, Zhengming Ding, and Yang Cong · 2024
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Fairtl: A transfer learning approach for bias mitigation in deep generative models
Christopher T. H. Teo, Milad Abdollahzadeh, and Ngai-Man Cheung · 2024
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Rejection sampling imle: Designing priors for better few-shot image synthesis
Chirag Vashist, Shichong Peng, and Ke Li · 2024
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Bridging data gaps in diffusion models with adversarial noise-based transfer learning
Xiyu Wang, Baijiong Lin, Daochang Liu, Ying-Cong Chen, and Chang Xu · 2024
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Domain re-modulation for few-shot generative domain adaptation
Yi Wu, Ziqiang Li, Chaoyue Wang, Heliang Zheng, Shanshan Zhao, Bin Li, and Dacheng Tao · 2024
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Fastcomposer: Tuning-free multi-subject image generation with localized attention
Guangxuan Xiao, Tianwei Yin, William T Freeman, Frédo Durand, and Song Han · 2024
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Dm-gan: Cnn hybrid vits for training gans under limited data
Longquan Yan, Ruixiang Yan, Bosong Chai, Guohua Geng, Pengbo Zhou, and Jian Gao · 2024
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Fontdiffuser: One-shot font generation via denoising diffusion with multi-scale content aggregation and style contrastive learning
Zhenhua Yang, Dezhi Peng, Yuxin Kong, Yuyi Zhang, Cong Yao, and Lianwen Jin · 2024
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A single-image gan model using self-attention mechanism and densenets
Eyyup Yildiz, Mehmet Erkan Yuksel, and Selcuk Sevgen · 2024
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Tf 2: Few-shot text-free training-free defect image generation for industrial anomaly inspection
Qianzi Yu, Kai Zhu, Yang Cao, Feijie Xia, and Yu Kang · 2024
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Few-shot learning based on deep learning: A survey
Wu Zeng and Zheng-ying Xiao · 2024
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Mutual information compensation for high-fidelity image generation with limited data
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Hybridbooth: Hybrid prompt inversion for efficient subject-driven generation
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Comfusion: Enhancing personalized generation by instance-scene compositing and fusion
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Bk-sdm: A lightweight, fast, and cheap version of stable diffusion
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Large language diffusion models
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Ziplora: Any subject in any style by effectively merging loras
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Moma: Multimodal llm adapter for fast personalized image generation
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Multigen: Zero-shot image generation from multi-modal prompts
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Semantic mask reconstruction and category semantic learning for few-shot image generation
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Logosticker: Inserting logos into diffusion models for customized generation
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