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Stable Distribution Alignment Using the Dual of the Adversarial Distance
[article]
2018
arXiv
pre-print
Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and
arXiv:1707.04046v4
fatcat:5qgicm6oqvbflntxx5kvb7pj4u