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We propose a method for semi-supervised training of structured-output neural networks.
Reducing the dimensionality of data with neural networks
Hinton, G. E., and Salakhutdinov, R. R · 2006
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
Ranzato, M., Huang, F., Boureau, Y., and LeCun, Y · 2007
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Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J., Meier, U., Cireşan, D., and Schmidhuber, J · 2011
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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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Very deep convolutional networks for large-scale image recognition
Simonyan, K., and Zisserman, A · 2014
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., and Cipolla, R · 2015
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. S · 2015
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Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., and Han, B · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Semi-Supervised Learning with Ladder Networks, Nov. 2015
Rasmus, A., Valpola, H., Honkala, M., Berglund, M., and Raiko, T · 2015
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From neural {PCA} to deep unsupervised learning
Valpola, H · 2015
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Stacked what-where auto-encoders
Zhao, J., Mathieu, M., Goroshin, R., and LeCun, Y · 2015
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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Judy Hoffman, Dequan Wang, F. Y., and Darrell, T · 2016
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Semantic segmentation using adversarial networks
Luc, P., Couprie, C., Chintala, S., and Verbeek, J · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M., and Favaro, P · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., and Efros, A. A · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X · 2016
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Donahue, J., Krähenbühl, P., and Darrell, T · 2016
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., and Courville, A · 2016
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2016
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Augmenting supervised neural networks with unsupervised objectives for large-scale image classification
Zhang, Y., Lee, K., and Lee, H · 2016
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Energy-based generative adversarial network
Zhao, J. J., Mathieu, M., and LeCun, Y · 2016
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Adaptive deconvolutional networks for mid and high level feature learning
Zeiler, M. D., Taylor, G. W., and Fergus, R · 2025
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