Semi-Supervised Learning via New Deep Network Inversion

12 Nov 2017Randall BalestrieroVincent RogerHerve G. GlotinRichard G. Baraniuk

We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach outperforms current state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while using $5$ labeled examples per class... (read more)

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