We consider the general $f$-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases. We present a new optimization method for this formulation, where the gradient is computed using an adversarially learned discriminator. In our framework, we show that different divergences induce similar algorithms in terms of gradient evaluation, except with different scaling. Therefore this paper gives a general recipe for a class of principled $f$-divergence based generative modeling methods. Theoretical justifications and extensive empirical studies are provided to demonstrate the advantage of our approach over existing methods.
5月15日
10:30am - 11:30am

地点
https://hkust.zoom.com.cn/j/96396217133
讲者/表演者
Ms. Xinwei SHEN
HKUST
HKUST
主办单位
Department of Mathematics
联系方法
mathseminar@ust.hk
付款详情
对象
Alumni, Faculty and Staff, PG Students, UG Students
语言
英语
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Abstract
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