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Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data.
Wasserstein Barycenter and Its Application to Texture Mixing
Rabin, J., Peyré, G., Delon, J., and Bernot, M · 2012
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., and Bengio, Y · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2015
Earlier work this paper cites.
Least Squares Generative Adversarial Networks
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., and Smolley, S. P · 2016
Earlier work this paper cites.
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
Interpretable deep neural networks for single-trial EEG classification
Sturm, I., Lapuschkin, S., Samek, W., and Müller, K.-R · 2016
Earlier work this paper cites.
WaveNet: A Generative Model for Raw Audio
van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Improved Training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
On Convergence and Stability of GANs
Kodali, N., Abernethy, J., Hays, J., and Kira, Z · 2017
Later among the works it cites.
Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., and Ball, T · 2017
Later among the works it cites.
Deep EEG super-resolution: Upsampling EEG spatial resolution with Generative Adversarial Networks
Corley, I. A. and Huang, Y · 2018
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Synthesizing Audio with Generative Adversarial Networks
Donahue, C., McAuley, J., and Puckette, M · 2018
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Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding
Hartmann, K. G., Schirrmeister, R. T., and Ball, T · 2018
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Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
Cited alongside, same era.
Learning how to explain neural networks: PatternNet and PatternAttribution
Kindermans, P.-J., Schütt, K. T., Alber, M., Müller, K.-R., Erhan, D., Kim, B., and Dähne, S · 2017
Cited alongside, same era.
Closest in time.
Computational Optimal Transport
Peyré, G. and Cuturi, M · 2018
Closest in time.