Wide Residual Networks

Sergey ZagoruykoNikos Komodakis

   Papers with code   Abstract  PDF

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train... (read more)

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