Friday, June 16, 2017

FreezeOut: Accelerate Training by Progressively Freezing Layers - implementation -

What initially looks like playing with hyperparameters brings new life to a somewhat older approach. From Alex's tweet:

The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. We empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets on CIFAR.

DenseNet is at: 
while FreezeOut is here: 

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