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Semi-Implicit Generative Model
To combine explicit and implicit generative models, we introduce semi-im...
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Adversarial Learning of a Sampler Based on an Unnormalized Distribution
We investigate adversarial learning in the case when only an unnormalize...
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The Inductive Bias of Restricted f-GANs
Generative adversarial networks are a novel method for statistical infer...
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Coverage and Quality Driven Training of Generative Image Models
Generative modeling of natural images has been extensively studied in re...
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The IMP game: Learnability, approximability and adversarial learning beyond Σ^0_1
We introduce a problem set-up we call the Iterated Matching Pennies (IMP...
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A Survey of Adversarial Learning on Graphs
Deep learning models on graphs have achieved remarkable performance in v...
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Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation
Simulators that generate observations based on theoretical models can be...
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Bridging Maximum Likelihood and Adversarial Learning via α-Divergence
Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learning encourages the capture of all data modes, and it is typically characterized by stable training. However, ML learning tends to distribute probability mass diffusely over the data space, e.g., yielding blurry synthetic images. Adversarial learning is well known to synthesize highly realistic natural images, despite practical challenges like mode dropping and delicate training. We propose an α-Bridge to unify the advantages of ML and adversarial learning, enabling the smooth transfer from one to the other via the α-divergence. We reveal that generalizations of the α-Bridge are closely related to approaches developed recently to regularize adversarial learning, providing insights into that prior work, and further understanding of why the α-Bridge performs well in practice.
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