Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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Linear learning-rate scaling plus warmup lets minibatch size 8192 train ResNet-50 on ImageNet in one hour at full small-batch accuracy.
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Deep Residual Learning for Image Recognition
Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Linear learning-rate scaling plus warmup lets minibatch size 8192 train ResNet-50 on ImageNet in one hour at full small-batch accuracy.