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Precision and Recall are two prominent metrics of generative performance, which were proposed to separately measure the fidelity and diversity of generative models.
When is “nearest neighbor” meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., and Shaft, U · 1999
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A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B., Silva, V. d., and Langford, J. C · 2000
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On the surprising behavior of distance metrics in high dimensional space
Aggarwal, C. C., Hinneburg, A., and Keim, D. A · 2001
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Taming the curse of dimensionality in kernels and novelty detection
Evangelista, P. F., Embrechts, M. J., and Szymanski, B. K · 2006
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The concentration of fractional distances
François, D., Wertz, V., and Verleysen, M · 2007
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On the local behavior of spaces of natural images
Carlsson, G., Ishkhanov, T., De Silva, V., and Zomorodian, A · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Image quality metrics: Psnr vs. ssim
Hore, A. and Ziou, D · 2010
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Hubs in space: Popular nearest neighbors in high-dimensional data
Radovanovic, M., Nanopoulos, A., and Ivanovic, M · 2010
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Concise formulas for the area and volume of a hyperspherical cap
Li, S · 2011
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
Which training methods for gans do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
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Efficient super resolution for large-scale images using attentional gan
Pathak, H. N., Li, X., Minaee, S., and Cowan, B · 2018
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Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Classification accuracy score for conditional generative models
Ravuri, S. and Vinyals, O · 2019
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Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in ct segmentation tasks
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Do GANs learn the distribution? some theory and empirics
Arora, S., Risteski, A., and Zhang, Y · 2018
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Demystifying MMD GANs
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
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A style-based generator architecture for generative adversarial networks. ieee
Karras, T., Laine, S., and Aila, T · 2018
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Disconnected manifold learning for generative adversarial networks
Khayatkhoei, M., Singh, M. K., and Elgammal, A · 2018
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
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Sandfort, V., Yan, K., Pickhardt, P. J., and Summers, R. M · 2019
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Fourier spectrum discrepancies in deep network generated images
Dzanic, T., Shah, K., and Witherden, F · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
Alaa, A., Van Breugel, B., Saveliev, E. S., and van der Schaar, M · 2022
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Spatial frequency bias in convolutional generative adversarial networks
Khayatkhoei, M. and Elgammal, A · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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