Fast Adaptation in Generative Models with Generative Matching Networks

no code implementations • 7 Dec 2016 • Sergey Bartunov, Dmitry P. Vetrov

Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples.

One-Shot Learning

M-Best-Diverse Labelings for Submodular Energies and Beyond

no code implementations • NeurIPS 2015 • Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy

In this work we show that the joint inference of $M$ best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used.

Total Energy