Inductive Transfer for Neural Architecture Optimization

Understand

The recent advent of automated neural network architecture search led to several methods that outperform state-of-the-art human-designed architectures.

• However, these approaches are computationally expensive, in extreme cases consuming GPU years.
• We propose two novel methods which aim to expedite this optimization problem by transferring knowledge acquired from previous tasks to new ones.
• First, we propose a novel neural architecture selection method which employs this knowledge to identify strong and weak characteristics of neural architectures across datasets.