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The Fr\'echet Inception Distance (FID) has been used to evaluate hundreds of generative models.
The Fréchet Distance between Multivariate Normal Distributions
Dowson, D. and Landau, B · 1982
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A Schur Method for the Square Root of a Matrix
Björck, Å. and Hammarling, S · 1983
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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 · 2009
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Blocked Schur Algorithms for Computing the Matrix Square Root
Deadman, E., Higham, N. J., and Ralha, R · 2012
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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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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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Compressing Neural Networks with the Hashing Trick
Chen, W., Wilson, J., Tyree, S., Weinberger, K., and Chen, Y · 2015
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NICE: Non-Linear Independent Components Estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
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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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Inceptionism: Going Deeper into Neural Networks, 2015
Mordvintsev, A., Olah, C., and Tyka, M · 2015
Cited alongside, same era.
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Cited alongside, same era.
Training Deep Nets with Sublinear Memory Cost
Chen, T., Xu, B., Zhang, C., and Guestrin, C · 2016
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Improved Bilinear Pooling with CNNs
Pros and Cons of GAN Evaluation Measures
Borji, A · 2019
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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The Low-Rank Eigenvalue Problem, 2019
Nakatsukasa, Y · 2019
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Pytorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Self-Supervised GAN: Analysis and Improvement With Multi-Class MiniMax Game
Tran, N.-T., Tran, V.-H., Nguyen, N.-B., Yang, L., and Cheung, N.-M · 2019
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Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A · 2019
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Lin, T.-Y. and Maji, S · 2017
Cited alongside, same era.
Glow: Generative Flow with Invertible 1x1 Convolutions
Kingma, D. P. and Dhariwal, P · 2018
Cited alongside, same era.
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
Cited alongside, same era.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., Kadurin, A., Nikolenko, S., Aspuru-Guzik, A., and Zhavoronkov, A · 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., and Klambauer, G · 2018
Cited alongside, same era.
Later among the works it cites.
Training Generative Adversarial Networks with Limited Data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2020
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PyTorch-FID: FID Score for PyTorch
Seitzer, M · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, İ., Feng, Y., Moore, E. W., Vand erPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and Contributors, S. . · 2020
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