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Generative Adversarial Networks (GANs) have been used in several machine learning tasks such as domain transfer, super resolution, and synthetic data generation.
Diatom autofocusing in brightfield microscopy: a comparative study
Pech-Pacheco, J., Cristobal, G., Chamorro-Martinez, J., and Fernandez-Valdivia, J · 2000
Earlier work this paper cites.
Information Theory, Inference & Learning Algorithms
MacKay, D. J. C · 2002
Earlier work this paper cites.
Model compression
Buciluǎ, C., Caruana, R., and Niculescu-Mizil, A · 2006
Earlier work this paper cites.
Do deep nets really need to be deep?
Ba, L. J. and Caurana, R · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Earlier work this paper cites.
A survey of model compression and acceleration for deep neural networks
Cheng, Y., Wang, D., Zhou, P., and Zhang, T · 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.
Transferring knowledge to smaller network with class-distance loss
Kim, S. W. and Kim, H.-E · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A. P., Tejani, A., Totz, J., Wang, Z., et al · 2017
Cited alongside, same era.
Training quantized nets: A deeper understanding
Li, H., De, S., Xu, Z., Studer, C., Samet, H., and Goldstein, T · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networkss
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
Later among the works it cites.
Adversarial network compression
Belagiannis, V., Farshad, A., and Galasso, F · 2018
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
Later among the works it cites.
Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Later among the works it cites.
Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
Later among the works it cites.
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Do deep convolutional nets really need to be deep and convolutional
Urban, G., Geras, K. J., Kahou, S. E., Aslan, O., Wang, S., Mohamed, A., Philipose, M., Richardson, M., and Caruana, R · 2017
Cited alongside, same era.
Learning loss for knowledge distillation with conditional adversarial networks
Xu, Z., Hsu, Y., and Huang, J · 2017
Cited alongside, same era.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Yim, J., Joo, D., Bae, J., and Kim, J · 2017
Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Allen-Zhu, Z., Li, Y., and Liang, Y
Cited in the paper.
A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z
Cited in the paper.
Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
Later among the works it cites.
Model compression with generative adversarial netwos, 2019
Liu, R., Fusi, N., and Mkey, L · 2019
Closest in time.