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Imagine generating a city's electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winter freeze.
Sur la distance de deux lois de probabilité
Fréchet, M · 1957
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2015
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Electricity Load Diagrams
Trindade, A · 2015
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Real-valued (medical) time series generation with recurrent conditional gans, 2017
Esteban, C., Hyland, S. L., and Rätsch, G · 2017
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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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Conditional image synthesis with auxiliary classifier gans
Odena, A., Olah, C., and Shlens, J · 2017
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Multimodal unsupervised image-to-image translation
Huang, X., Liu, M.-Y., Belongie, S., and Kautz, J · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and van der Schaar, M · 2018
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An improved evaluation framework for generative adversarial networks
Liu, S., Wei, Y., Lu, J., and Zhou, J · 2018
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cgans with projection discriminator
Miyato, T. and Koyama, M · 2018
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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
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Towards accurate generative models of video: A new metric & challenges
Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., and Gelly, S · 2018
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Beijing Multi-Site Air-Quality Data
Chen, S · 2019
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On the evaluation of conditional GANs
DeVries, T., Romero, A., Pineda, L., Taylor, G. W., and Drozdzal, M · 2019
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Adversarial audio synthesis
Donahue, C., McAuley, J., and Puckette, M · 2019
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Metro Interstate Traffic Volume
Hogue, J · 2019
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PfaGAN: An aesthetics-conditional GAN for generating photographic fine art
Murray, N · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Time-series generative adversarial networks
Yoon, J., Jarrett, D., and van der Schaar, M · 2019
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Evaluation metrics for conditional image generation
Benny, Y., Galanti, T., Benaim, S., and Wolf, L · 2020
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
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Ccgan: Continuous conditional generative adversarial networks for image generation
Ding, X., Wang, Y., Xu, Z., Welch, W. J., and Wang, Z. J · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
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Deepfake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Thambawita, V., Isaksen, J. L., Hicks, S. A., Ghouse, J., Ahlberg, G., Linneberg, A., Grarup, N., Ellervik, C., Olesen, M. S., Hansen, T., Graff, C., Holstein-Rathlou, N.-H., Strümke, I., Hammer, H. L., Maleckar, M. M., Halvorsen, P., Riegler, M. A., and Kanters, J. K · 2021
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Multimodal contrastive training for visual representation learning
Yuan, X., Lin, Z. L., Kuen, J., Zhang, J., Wang, Y., Maire, M., Kale, A., and Faieta, B · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 2021
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Synthesizing adversarial visual scenarios for model-based robotic control
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Using gans for sharing networked time series data: Challenges, initial promise, and open questions
Lin, Z., Jain, A., Wang, C., Fanti, G., and Sekar, V · 2020
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Ptb-xl, a large publicly available electrocardiography dataset
Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., and Schaeffter, T · 2020
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Anonymization through data synthesis using generative adversarial networks (ads-gan)
Yoon, J., Drumright, L., and Schaar, M · 2020
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A methodology for validating diversity in synthetic time series generation
Bahrpeyma, F., Roantree, M., Cappellari, P., Scriney, M., and McCarren, A · 2021
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Challenges and corresponding solutions of generative adversarial networks (gans): A survey study
Chen, H · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Agarwal, S. and Chinchali, S. P · 2022
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Diffusion-based time series imputation and forecasting with structured state space models
Alcaraz, J. L. and Strodthoff, N · 2022
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Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Re, C · 2022
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Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., and Fleet, D. J · 2022
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Tts-gan: A transformer-based time-series generative adversarial network
Li, X., Metsis, V., Wang, H., and Ngu, A. H. H · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Gool, L. V · 2022
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Hierarchical text-conditional image generation with clip latents, 2022
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Stress testing electrical grids: Generative adversarial networks for load scenario generation
Rizzato, M., Morizet, N., Maréchal, W., and Geissler, C · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Conditional frechet inception distance, 2022
Soloveitchik, M., Diskin, T., Morin, E., and Wiesel, A · 2022
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Contrastive learning of medical visual representations from paired images and text
Zhang, Y., Jiang, H., Miura, Y., Manning, C. D., and Langlotz, C. P · 2022
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Diffusion-based conditional ecg generation with structured state space models
Alcaraz, J. M. L. and Strodthoff, N · 2023
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Saits: Self-attention-based imputation for time series
Du, W., Côté, D., and Liu, Y · 2023
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Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation
Wu*, Y., Chen*, K., Zhang*, T., Hui*, Y., Berg-Kirkpatrick, T., and Dubnov, S · 2023
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