Adversarial Ladder Networks

Bibliographic Details
Title: Adversarial Ladder Networks
Authors: Molano, Juan Maroñas, Colomer, Alberto Albiol, Palacios, Roberto Paredes
Publication Year: 2016
Collection: Computer Science
Statistics
Subject Terms: Computer Science - Neural and Evolutionary Computing, Computer Science - Learning, Statistics - Machine Learning
More Details: The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializing the weights of the network and after these weights are refined with the use of supervised data. On the other hand adversarial noise has improved the results of clas- sical supervised learning. Recently, a new neural network topology called Ladder Network, where the key idea is based in some properties of hierar- chichal latent variable models, has been proposed as a technique to train a neural network using supervised and unsupervised data at the same time with what is called semi-supervised learning. This technique has reached state of the art classification. In this work we add adversarial noise to the ladder network and get state of the art classification, with several important conclusions on how adversarial noise can help in addition with new possible lines of investi- gation. We also propose an alternative to add adversarial noise to unsu- pervised data.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/1611.02320
Accession Number: edsarx.1611.02320
Database: arXiv
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  Data: The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializing the weights of the network and after these weights are refined with the use of supervised data. On the other hand adversarial noise has improved the results of clas- sical supervised learning. Recently, a new neural network topology called Ladder Network, where the key idea is based in some properties of hierar- chichal latent variable models, has been proposed as a technique to train a neural network using supervised and unsupervised data at the same time with what is called semi-supervised learning. This technique has reached state of the art classification. In this work we add adversarial noise to the ladder network and get state of the art classification, with several important conclusions on how adversarial noise can help in addition with new possible lines of investi- gation. We also propose an alternative to add adversarial noise to unsu- pervised data.
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      – SubjectFull: Computer Science - Neural and Evolutionary Computing
        Type: general
      – SubjectFull: Computer Science - Learning
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      – SubjectFull: Statistics - Machine Learning
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      – TitleFull: Adversarial Ladder Networks
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            NameFull: Molano, Juan Maroñas
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            NameFull: Colomer, Alberto Albiol
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            NameFull: Palacios, Roberto Paredes
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              Y: 2016
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