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A good metric, which promises a reliable comparison between solutions, is essential for any well-defined task.
The fréchet distance between multivariate normal distributions
D. Dowson and B. Landau · 1982
Earlier work this paper cites.
On kernel-target alignment
N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. Kandola · 2001
Earlier work this paper cites.
Measuring statistical dependence with hilbert-schmidt norms
A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Algorithms for learning kernels based on centered alignment
C. Cortes, M. Mohri, and A. Rostamizadeh · 2012
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Earlier work this paper cites.
Demystifying MMD GANs
M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
Earlier work this paper cites.
Multimodal unsupervised image-to-image translation
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz · 2018
Earlier work this paper cites.
Assessing generative models via precision and recall
M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
Earlier work this paper cites.
Similarity of neural network representations revisited
S. Kornblith, M. Norouzi, H. Lee, and G. Hinton · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila · 2019
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
Cited alongside, same era.
Contrastive learning for unpaired image-to-image translation
T. Park, A. A. Efros, R. Zhang, and J.-Y. Zhu · 2020
Cited alongside, same era.
Instance-conditioned gan
A. Casanova, M. Careil, J. Verbeek, M. Drozdzal, and A. Romero-Soriano · 2021
Cited alongside, same era.
An empirical study of training self-supervised vision transformers
X. Chen, S. Xie, and K. He · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
BIGRoc: Boosting image generation via a robust classifier
R. Ganz and M. Elad · 2022
Later among the works it cites.
Ensembling off-the-shelf models for gan training
N. Kumari, R. Zhang, E. Shechtman, and J.-Y. Zhu · 2022
Later among the works it cites.
The role of imagenet classes in fr \ \backslash ’echet inception distance
T. Kynkäänniemi, T. Karras, M. Aittala, T. Aila, and J. Lehtinen · 2022
Later among the works it cites.
Trend: Truncated generalized normal density estimation of inception embeddings for gan evaluation
J. Lee and J.-S. Lee · 2022
Later among the works it cites.
A convnet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
Later among the works it cites.
On aliased resizing and surprising subtleties in gan evaluation
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Repvgg: Making vgg-style convnets great again
X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun · 2021
Cited alongside, same era.
Alias-free generative adversarial networks
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, and T. Aila · 2021
Cited alongside, same era.
On self-supervised image representations for gan evaluation
S. Morozov, A. Voynov, and A. Babenko · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Cited alongside, same era.
Do vision transformers see like convolutional neural networks?
M. Raghu, T. Unterthiner, S. Kornblith, C. Zhang, and A. Dosovitskiy · 2021
Cited alongside, same era.
Projected gans converge faster
A. Sauer, K. Chitta, J. Müller, and A. Geiger · 2021
Cited alongside, same era.
G. Parmar, R. Zhang, and J.-Y. Zhu · 2022
Later among the works it cites.
Scalable diffusion models with transformers
W. Peebles and S. Xie · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
Later among the works it cites.
Stylegan-xl: Scaling stylegan to large diverse datasets
A. Sauer, K. Schwarz, and A. Geiger · 2022
Later among the works it cites.
Resmlp: Feedforward networks for image classification with data-efficient training
H. Touvron, P. Bojanowski, M. Caron, M. Cord, A. El-Nouby, E. Grave, G. Izacard, A. Joulin, G. Synnaeve, J. Verbeek, et al · 2022
Later among the works it cites.
Vision transformer with deformable attention
Z. Xia, X. Pan, S. Song, L. E. Li, and G. Huang · 2022
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Glead: Improving gans with a generator-leading task
Q. Bai, C. Yang, Y. Xu, X. Liu, Y. Yang, and Y. Shen · 2023
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Masked diffusion transformer is a strong image synthesizer
S. Gao, P. Zhou, M.-M. Cheng, and S. Yan · 2023
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Composer: Creative and controllable image synthesis with composable conditions
L. Huang, D. Chen, Y. Liu, Y. Shen, D. Zhao, and J. Zhou · 2023
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Feature likelihood score: Evaluating generalization of generative models using samples
M. Jiralerspong, A. J. Bose, and G. Gidel · 2023
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Refining generative process with discriminator guidance in score-based diffusion models
D. Kim, Y. Kim, W. Kang, and I.-C. Moon · 2023
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Improving gan training via feature space shrinkage
H. Liu, W. Zhang, B. Li, H. Wu, N. He, Y. Huang, Y. Li, B. Ghanem, and Y. Zheng · 2023
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C. Mou, X. Wang, L. Xie, J. Zhang, Z. Qi, Y. Shan, and X. Qie · 2023
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Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis
A. Sauer, T. Karras, S. Laine, A. Geiger, and T. Aila · 2023
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Adding conditional control to text-to-image diffusion models
L. Zhang and M. Agrawala · 2023
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