GradMax: Growing Neural Networks using Gradient InformationDownload PDF

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Sep 29, 2021 (edited Oct 05, 2021)ICLR 2022 Conference Blind SubmissionReaders: Everyone
  • Keywords: efficient training, efficient, computer vision, architecture search
  • Abstract: The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified. In this work we instead focus on growing the architecture without requiring costly retraining. We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics. We do this by maximizing the gradients of the new neurons and find an approximation to the optimal initialization by means of the singular value decomposition (SVD). We call this technique Gradient Maximizing Growth (GradMax) and demonstrate its effectiveness in variety of vision tasks and architectures.
  • One-sentence Summary: We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics.
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