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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T., 2019 · 1904
Earlier work this paper cites.
Quality evaluation of gans using cross local intrinsic dimensionality
Barua, S., Ma, X., Erfani, S.M., Houle, M.E., Bailey, J., 2019 · 1905
Earlier work this paper cites.
Revisiting precision and recall definition for generative model evaluation
Simon, L., Webster, R., Rabin, J., 2019 · 1905
Earlier work this paper cites.
The shape of data: Intrinsic distance for data distributions
Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Bronstein, A., Oseledets, I., Müller, E., 2019 · 1905
Earlier work this paper cites.
Characterizing bias in classifiers using generative models
McDuff, D., Ma, S., Song, Y., Kapoor, A., 2019 · 1906
Earlier work this paper cites.
Generating diverse high-fidelity images with vq-vae-2
Razavi, A., Oord, A.v.d., Vinyals, O., 2019 · 1906
Earlier work this paper cites.
Generative adversarial networks in computer vision: A survey and taxonomy
Wang, Z., She, Q., Ward, T.E., 2019 · 1906
Earlier work this paper cites.
On the”steerability" of generative adversarial networks
Jahanian, A., Chai, L., Isola, P., 2019 · 1907
Earlier work this paper cites.
On the anomalous generalization of gans
Xuan, J., Yang, Y., Yang, Z., He, D., Wang, L., 2019 · 1909
Earlier work this paper cites.
Fourier spectrum discrepancies in deep network generated images
Dzanic, T., Shah, K., Witherden, F., 2019 · 1911
Earlier work this paper cites.
Approximating human judgment of generated image quality
Kolchinski, Y.A., Zhou, S., Zhao, S., Gordon, M., Ermon, S., 2019 · 1912
Earlier work this paper cites.
Animating rotation with quaternion curves, in: Proceedings of the 12th annual conference on Computer graphics and interactive techniques, pp. 245–254
Shoemake, K., 1985 · 1985
Earlier work this paper cites.
Towards gan benchmarks which require generalization
Gulrajani, I., Raffel, C., Metz, L., 2020 · 2001
Earlier work this paper cites.
Reliable fidelity and diversity metrics for generative models
Naeem, M.F., Oh, S.J., Uh, Y., Choi, Y., Yoo, J., 2020 · 2002
Earlier work this paper cites.
A non-parametric test to detect data-copying in generative models
Meehan, C., Chaudhuri, K., Dasgupta, S., 2020 · 2004
Earlier work this paper cites.
Mimicry: Towards the reproducibility of gan research
Lee, K.S., Town, C., 2020 · 2005
Earlier work this paper cites.
Fast fr \ \backslash ’echet inception distance
Mathiasen, A., Hvilshøj, F., 2020 · 2009
Earlier work this paper cites.
A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection, in: 2009 International Workshop on Quality of Multimedia Experience, IEEE. pp. 87–91
Narvekar, N.D., Karam, L.J., 2009 · 2009
Earlier work this paper cites.
Generating unseen complex scenes: are we there yet?
Casanova, A., Drozdzal, M., Romero-Soriano, A., 2020 · 2012
Earlier work this paper cites.
Image sharpness measure for blurred images in frequency domain
De, K., Masilamani, V., 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D.P., Welling, M., 2013 · 2013
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A., 2014 · 2014
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E., Chintala, S., Szlam, A., Fergus, R., 2015 · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild, in: Proceedings of the IEEE international conference on computer vision, pp. 3730–3738
Liu, Z., Luo, P., Wang, X., Tang, X., 2015 · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L., Oord, A.v.d., Bethge, M., 2015 · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J., 2015 · 2015
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Oord, A.v.d., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., Kavukcuoglu, K., 2016 · 2016
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., 2016 · 2016
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset, in: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308
Carreira, J., Zisserman, A., 2017 · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S., 2017 · 2017
Earlier work this paper cites.
Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O., 2017 · 2017
Cited alongside, same era.
Statistics of deep generated images
Zeng, Y., Lu, H., Borji, A., 2017 · 2017
Cited alongside, same era.
Barratt, S., Sharma, R., 2018 · 2018
Cited alongside, same era.
Gan dissection: Visualizing and understanding generative adversarial networks
Bau, D., Zhu, J.Y., Strobelt, H., Zhou, B., Tenenbaum, J.B., Freeman, W.T., Torralba, A., 2018 · 2018
Cited alongside, same era.
Fvd: A new metric for video generation
Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., Gelly, S., 2019 · 2019
Later among the works it cites.
Attributing fake images to gans: Learning and analyzing gan fingerprints, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7556–7566
Yu, N., Davis, L.S., Fritz, M., 2019 · 2019
Later among the works it cites.
Zhou, S., Gordon, M., Krishna, R., Narcomey, A., Fei-Fei, L.F., Bernstein, M., 2019 · 2019
Later among the works it cites.
What makes fake images detectable? understanding properties that generalize, in: European Conference on Computer Vision, Springer. pp. 103–120
Chai, L., Bau, D., Lim, S.N., Isola, P., 2020 · 2020
Later among the works it cites.
Effectively unbiased fid and inception score and where to find them, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6070–6079
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Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A., 2018 · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., Simonyan, K., 2018 · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification, in: Conference on fairness, accountability and transparency, PMLR. pp. 77–91
Buolamwini, J., Gebru, T., 2018 · 2018
Cited alongside, same era.
Lip movements generation at a glance, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 520–535
Chen, L., Li, Z., Maddox, R.K., Duan, Z., Xu, C., 2018 · 2018
Cited alongside, same era.
Geometry score: A method for comparing generative adversarial networks, in: International Conference on Machine Learning, PMLR. pp. 2621–2629
Khrulkov, V., Oseledets, I., 2018 · 2018
Cited alongside, same era.
An improved evaluation framework for generative adversarial networks
Liu, S., Wei, Y., Lu, J., Zhou, J., 2018 · 2018
Cited alongside, same era.
Evaluating generative adversarial networks on explicitly parameterized distributions
O’Brien, S., Groh, M., Dubey, A., 2018 · 2018
Cited alongside, same era.
Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., Klambauer, G., 2018 · 2018
Cited alongside, same era.
Chong, M.J., Forsyth, D., 2020 · 2020
Later among the works it cites.
Precision-recall curves using information divergence frontiers, in: International Conference on Artificial Intelligence and Statistics, pp. 2550–2559
Djolonga, J., Lucic, M., Cuturi, M., Bachem, O., Bousquet, O., Gelly, S., 2020 · 2020
Later among the works it cites.
Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7890–7899
Durall, R., Keuper, M., Keuper, J., 2020 · 2020
Later among the works it cites.
Leveraging frequency analysis for deep fake image recognition, in: International Conference on Machine Learning, PMLR. pp. 3247–3258
Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., Holz, T., 2020 · 2020
Later among the works it cites.
The survey: Text generation models in deep learning
Iqbal, T., Qureshi, S., 2020 · 2020
Later among the works it cites.
Analyzing and improving the image quality of stylegan, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T., 2020 · 2020
Later among the works it cites.
On self-supervised image representations for gan evaluation, in: International Conference on Learning Representations
Morozov, S., Voynov, A., Babenko, A., 2020 · 2020
Later among the works it cites.
A review on deep learning techniques for video prediction
Oprea, S., Martinez-Gonzalez, P., Garcia-Garcia, A., Castro-Vargas, J.A., Orts-Escolano, S., Garcia-Rodriguez, J., Argyros, A., 2020 · 2020
Later among the works it cites.
Investigating object compositionality in generative adversarial networks
van Steenkiste, S., Kurach, K., Schmidhuber, J., Gelly, S., 2020 · 2020
Later among the works it cites.
Deepfakes and beyond: A survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., Ortega-Garcia, J., 2020 · 2020
Later among the works it cites.
Inclusive gan: Improving data and minority coverage in generative models, in: European Conference on Computer Vision, Springer. pp. 377–393
Yu, N., Li, K., Zhou, P., Malik, J., Davis, L., Fritz, M., 2020 · 2020
Later among the works it cites.
Alaa, A.M., van Breugel, B., Saveliev, E., van der Schaar, M., 2021 · 2021
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Bai, C.Y., Lin, H.T., Raffel, C., Kan, W.C.w., 2021 · 2021
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Manifold topology divergence: a framework for comparing data manifolds
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Bond-Taylor, S., Leach, A., Long, Y., Willcocks, C.G., 2021 · 2021
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On memorization in probabilistic deep generative models
van den Burg, G.J., Williams, C.K., 2021 · 2021
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Cogview: Mastering text-to-image generation via transformers
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Are gan generated images easy to detect? a critical analysis of the state-of-the-art
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Generative adversarial transformers
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Transgan: Two transformers can make one strong gan
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Generative adversarial networks for image and video synthesis: Algorithms and applications
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Generating images with sparse representations
Nash, C., Menick, J., Dieleman, S., Battaglia, P.W., 2021 · 2021
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On buggy resizing libraries and surprising subtleties in fid calculation
Parmar, G., Zhang, R., Zhu, J.Y., 2021 · 2021
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Zero-shot text-to-image generation
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