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arXiv:1706.04983 (stat)
[Submitted on 15 Jun 2017 (v1), last revised 18 Jun 2017 (this version, v2)]

Title:FreezeOut: Accelerate Training by Progressively Freezing Layers

Authors:Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston
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Abstract: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. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at this https URL
Comments: Extended Abstract
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:1706.04983 [stat.ML]
  (or arXiv:1706.04983v2 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.1706.04983

Submission history

From: Andrew Brock [view email]
[v1] Thu, 15 Jun 2017 17:35:15 UTC (3,224 KB)
[v2] Sun, 18 Jun 2017 16:15:21 UTC (3,273 KB)
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