Generative Adversarial Networks

@article{Goodfellow2014GenerativeAN,
  title={Generative Adversarial Networks},
  author={Ian J. Goodfellow and Jean Pouget-Abadie and M. Mirza and B. Xu and David Warde-Farley and Sherjil Ozair and Aaron C. Courville and Yoshua Bengio},
  journal={ArXiv},
  year={2014},
  volume={abs/1406.2661}
}
  • Ian J. Goodfellow, Jean Pouget-Abadie, +5 authors Yoshua Bengio
  • Published 2014
  • Computer Science, Mathematics
  • ArXiv
  • We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a… CONTINUE READING
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    References

    SHOWING 1-10 OF 41 REFERENCES
    Generative Moment Matching Networks
    • 511
    • PDF
    A Generative Process for sampling Contractive Auto-Encoders
    • 65
    Generalized Denoising Auto-Encoders as Generative Models
    • 367
    • PDF
    A note on the evaluation of generative models
    • 653
    • PDF
    Learning Multiple Layers of Features from Tiny Images
    • 10,036
    • PDF
    Auto-Encoding Variational Bayes
    • 10,527
    • PDF
    A Fast Learning Algorithm for Deep Belief Nets
    • 11,500
    • PDF
    Stochastic Backpropagation and Approximate Inference in Deep Generative Models
    • 2,938
    • PDF
    Deep Boltzmann Machines
    • 1,784
    • PDF