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We systematically study a wide variety of generative models spanning semantically-diverse image datasets to understand and improve the feature extractors and metrics used to evaluate them.
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A kernel two-sample test
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Comparing Person- and Process-Centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk
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Variational inference with normalizing flows
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Deep unsupervised learning using nonequilibrium thermodynamics
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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
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Rethinking the Inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
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Improved Training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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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
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Stacked generative adversarial networks
X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie · 2017
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Y. Liu, Z. Qin, Z. Luo, and H. Wang · 2017
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Conditional image synthesis with auxiliary classifier GANs
A. Odena, C. Olah, and J. Shlens · 2017
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Learning to generate images with perceptual similarity metrics
J. Snell, K. Ridgeway, R. Liao, B. D. Roads, M. C. Mozer, and R. S. Zemel · 2017
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Deep feature interpolation for image content changes
P. Upchurch, J. Gardner, G. Pleiss, R. Pless, N. Snavely, K. Bala, and K. Weinberger · 2017
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StackGAN: Text to Photo-Realistic Image Synthesis With Stacked Generative Adversarial Networks
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas · 2017
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Demystifying MMD GANs
M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
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Semantically decomposing the latent spaces of generative adversarial networks
C. Donahue, A. Balsubramani, J. McAuley, and Z. C. Lipton · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
A. Jacot, F. Gabriel, and C. Hongler · 2018
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Image Generation from Sketch Constraint Using Contextual GAN
Y. Lu, S. Wu, Y.-W. Tai, and C.-K. Tang · 2018
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Assessing generative models via precision and recall
M. S. M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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Pros and cons of GAN evaluation measures
A. Borji · 2019
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Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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Residual flows for invertible generative modeling
R. T. Q. Chen, J. Behrmann, D. K. Duvenaud, and J.-H. Jacobsen · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
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Improved precision and recall metric for assessing generative models
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila · 2019
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PsychoPy2: Experiments in behavior made easy
J. Peirce, J. R. Gray, S. Simpson, M. MacAskill, R. Höchenberger, H. Sogo, E. Kastman, and J. K. Lindeløv · 2019
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Classification accuracy score for conditional generative models
S. Ravuri and O. Vinyals · 2019
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Metropolis-Hastings generative adversarial networks
R. Turner, J. Hung, E. Frank, Y. Saatchi, and J. Yosinski · 2019
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LOGAN: Latent optimisation for generative adversarial networks
Y. Wu, J. Donahue, D. Balduzzi, K. Simonyan, and T. Lillicrap · 2019
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HYPE: A benchmark for Human eYe Perceptual Evaluation of generative models
S. Zhou, M. Gordon, R. Krishna, A. Narcomey, L. Fei-Fei, and M. Bernstein · 2019
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Do we train on test data? Purging CIFAR of near-duplicates
B. Barz and J. Denzler · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2020
Cited alongside, same era.
Effectively unbiased FID and inception score and where to find them
M. J. Chong and D. Forsyth · 2020
Cited alongside, same era.
Geometric data analysis, beyond convolutions
Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes
S. Bond-Taylor, P. Hessey, H. Sasaki, T. P. Breckon, and C. G. Willcocks · 2022
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Pros and cons of GAN evaluation measures: New developments
A. Borji · 2022
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Dataset distillation by matching training trajectories
G. Cazenavette, T. Wang, A. Torralba, A. A. Efros, and J.-Y. Zhu · 2022
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MaskGIT: Masked generative image transformer
H. Chang, H. Zhang, L. Jiang, C. Liu, and W. T. Freeman · 2022
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The Vendi Score: A diversity evaluation metric for machine learning
D. Friedman and A. B. Dieng · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Feydy · 2020
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Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
Cited alongside, same era.
Bootstrap your own latent - a new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko · 2020
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Augmix: A simple method to improve robustness and uncertainty under data shift
D. Hendrycks, N. Mu, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan · 2020
Cited alongside, same era.
The origins and prevalence of texture bias in convolutional neural networks
K. Hermann, T. Chen, and S. Kornblith · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila · 2020
Cited alongside, same era.
Rarity score: A new metric to evaluate the uncommonness of synthesized images
J. Han, H. Choi, Y. Choi, J. Kim, J.-W. Ha, and J. Choi · 2022
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L. Hazami, R. Mama, and R. Thurairatnam · 2022
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Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2022
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StudioGAN: A taxonomy and benchmark of GANs for image synthesis
M. Kang, J. Shin, and J. Park · 2022
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Do better imagenet classifiers assess perceptual similarity better?
M. Kumar, N. Houlsby, N. Kalchbrenner, and E. D. Cubuk · 2022
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Autoregressive image generation using residual quantization
D. Lee, C. Kim, S. Kim, M. Cho, and W.-S. Han · 2022
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A ConvNet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
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On aliased resizing and surprising subtleties in GAN evaluation
G. Parmar, R. Zhang, and J.-Y. Zhu · 2022
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Scalable diffusion models with transformers
W. Peebles and S. Xie · 2022
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Hierarchical text-conditional image generation with CLIP latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al · 2022
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StyleGAN-XL: Scaling StyleGAN to large diverse datasets
A. Sauer, K. Schwarz, and A. Geiger · 2022
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Diffusion art or digital forgery? Investigating data replication in diffusion models
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein · 2022
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StyleNAT: Giving each head a new perspective
S. Walton, A. Hassani, X. Xu, Z. Wang, and H. Shi · 2022
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Diffusion-GAN: Training GANs with diffusion
Z. Wang, H. Zheng, P. He, W. Chen, and M. Zhou · 2022
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StyleSwin: Transformer-Based GAN for High-Resolution Image Generation
B. Zhang, S. Gu, B. Zhang, J. Bao, D. Chen, F. Wen, Y. Wang, and B. Guo · 2022
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iBOT: Image BERT pre-training with online tokenizer
J. Zhou, C. Wei, H. Wang, W. Shen, C. Xie, A. Yuille, and T. Kong · 2022
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Synthetic data from diffusion models improves imagenet classification
S. Azizi, S. Kornblith, C. Saharia, M. Norouzi, and D. J. Fleet · 2023
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A cookbook of self-supervised learning
R. Balestriero, M. Ibrahim, V. Sobal, A. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian, A. Schwarzschild, A. G. Wilson, J. Geiping, Q. Garrido, P. Fernandez, A. Bar, H. Pirsiavash, Y. LeCun, and M. Goldblum · 2023
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Extracting training data from diffusion models
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramer, B. Balle, D. Ippolito, and E. Wallace · 2023
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Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA
B. D. Douglas, P. J. Ewell, and M. Brauer · 2023
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Dreamsim: Learning new dimensions of human visual similarity using synthetic data
S. Fu, N. Tamir, S. Sundaram, L. Chai, R. Zhang, T. Dekel, and P. Isola · 2023
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DataComp: In search of the next generation of multimodal datasets
S. Y. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, E. Orgad, R. Entezari, S. Daras, Giannis adn Pratt, V. Ramanujan, Y. Bitton, K. Marathe, S. Mussmann, R. Vencu, M. Cherti, R. Krishna, P. W. Koh, O. Saukh, A. Ratner, S. Song, H. Hajishirzi, A. Farhadi, R. Beaumont, S. Oh, A. Dimakis, J. Jitsev, Y. Carmon, V. Shankar, and L. Schmidt · 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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Scaling up GANs for text-to-image synthesis
M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park · 2023
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M. Khayatkhoei and W. AbdAlmageed · 2023
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The role of ImageNet classes in Fréchet Inception Distance
T. Kynkäänniemi, T. Karras, M. Aittala, T. Aila, and J. Lehtinen · 2023
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DINOv2: Learning robust visual features without supervision
M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, R. Howes, P.-Y. Huang, H. Xu, V. Sharma, S.-W. Li, W. Galuba, M. Rabbat, M. Assran, N. Ballas, G. Synnaeve, I. Misra, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski · 2023
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Toward verifiable and reproducible human evaluation for text-to-image generation
M. Otani, R. Togashi, Y. Sawai, R. Ishigami, Y. Nakashima, E. Rahtu, J. Heikkilä, and S. Satoh · 2023
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https://www.pavlovia.org/, 2023
Pavlovia · 2023
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https://www.prolific.com/, 2023
Prolific · 2023
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Y. Song, P. Dhariwal, M. Chen, and I. Sutskever · 2023
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PFGM++: Unlocking the potential of physics-inspired generative models
Y. Xu, Z. Liu, Y. Tian, S. Tong, M. Tegmark, and T. Jaakkola · 2023
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Revisiting the evaluation of image synthesis with GANs
M. Yang, C. Yang, Y. Zhang, Q. Bai, Y. Shen, and B. Dai · 2023
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