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We propose a Randomized Progressive Training algorithm (RPT) -- a stochasticproxy for the well-known Progressive Training method (PT) (Karras et al.,2017). Originally designed to train GANs (Goodfellow et al., 2014), PT wasproposed as a heuristic, with no convergence analysis even for the simplestobjective functions. On the contrary, to the best of our knowledge, RPT is thefirst PT-type algorithm with rigorous and sound theoretical guarantees forgeneral smooth objective functions. We cast our method into the establishedframework of Randomized Coordinate Descent (RCD) (Nesterov, 2012; Richt\'arik &Tak\'a\v{c}, 2014), for which (as a by-product of our investigations) we alsopropose a novel, simple and general convergence analysis encapsulatingstrongly-convex, convex and nonconvex objectives. We then use this framework toestablish a convergence theory for RPT. Finally, we validate the effectivenessof our method through extensive computational experiments.

Understanding Progressive Training Through the Framework of Randomized Coordinate Descent