On distinguishability criteria for estimating generative models

3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings

Volumn , Issue , 2015, Pages

  Goodfellow, Ian J ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL STORAGE; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD ESTIMATION;

ADVERSARIAL NETWORKS; BINARY CLASSIFICATION; DISTINGUISHABILITY; DYNAMIC GENERATORS; ESTIMATION PROCEDURES; NETWORK LEARNING; NONSTATIONARY NOISE; TRAINING ALGORITHMS;

DISCRIMINATORS;

EID: 85083953782     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (2)

1. Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua. Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (eds.), Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc., 2014. URL http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.
2. Gutmann, M. and Hyvarinen, A. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. 2010.