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Generative adversarial networks (GANs) are successful deep generative models.
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Direct density ratio estimation with convolutional neural networks with application in outlier detection
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Foundation of Machine Learning
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On distinguishability criteria for estimating generative models
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Linking losses for density ratio and class-probability estimation
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Learning in Implicit Generative Models
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Improved Techniques for Training GANs
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