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Tunable Dual-Objective GANs for Stable Training
Monica Welfert · Kyle Otstot · Gowtham Kurri · Lalitha Sankar
Event URL: https://openreview.net/forum?id=w5wvlU2G0e »
In an effort to address the training instabilities of GANs, we introduce a class of dual-objective GANs with different value functions (objectives) for the generator (G) and discriminator (D). In particular, we model each objective using $\alpha$-loss, a tunable classification loss, to obtain $(\alpha_D,\alpha_G)$-GANs, parameterized by $(\alpha_D,\alpha_G)\in (0,\infty]^2$. For sufficiently large number of samples and capacities for G and D, we show that the resulting non-zero sum game simplifies to minimizing an $f$-divergence under appropriate conditions on $(\alpha_D,\alpha_G)$. We highlight the value of tuning $(\alpha_D,\alpha_G)$ in alleviating training instabilities for the synthetic 2D Gaussian mixture ring, the Celeb-A, and the LSUN Classroom datasets.

Author Information

Monica Welfert (Arizona State University)
Kyle Otstot (Arizona State University)
Gowtham Kurri (Arizona State University)
Lalitha Sankar (Arizona State University)

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