Geometric Enclosing Networks

Le, Trung; Vu, Hung; Nguyen, Tu Dinh; Phung, Dinh

doi:10.48550/arxiv.1708.04733

Cited by 1 publication

(1 citation statement)

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“…Another approach is to estimate the data distribution using an implicit density function, without the need for analytical forms of p model (e.g., see [11] for further discussions). An idea is to borrow the principle of minimal enclosing ball [26] to train a generator in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere [27]. However, the most notably pioneered class of this approach is the generative adversarial network (GAN) [10], an expressive generative model that is capable of producing sharp and realistic images for natural scenes.…”

Section: Introductionmentioning

confidence: 99%

Dual Discriminator Generative Adversarial Nets

Nguyen¹,

Le²,

Vu³

et al. 2017

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We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very effective, especially in addressing some key limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and reverse KL divergences into a unified objective function, thus it exploits the complementary statistical properties from these divergences to effectively diversify the estimated density in capturing multi-modes. We term our method dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has two discriminators; and together with a generator, it also has the analogy of a minimax game, wherein a discriminator rewards high scores for samples from data distribution whilst another discriminator, conversely, favoring data from the generator, and the generator produces data to fool both two discriminators. We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem. We conduct extensive experiments on synthetic and real-world large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made our best effort to compare our D2GAN with the latest state-of-the-art GAN's variants in comprehensive qualitative and quantitative evaluations. The experimental results demonstrate the competitive and superior performance of our approach in generating good quality and diverse samples over baselines, and the capability of our method to scale up to ImageNet database.

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Section: Introductionmentioning

confidence: 99%