Inductive Transfer for Neural Architecture Optimization

By Martin Wistuba, Tejaswini Pedapati. 2019

9 citations recorded by the source corpus.

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. Thus, these characteristics do not need to be rediscovered in every search, a strong weakness of current state-of-the-art searches. Second, we propose a method for learning curve extrapolation to determine if a training process can be terminated early. In contrast to existing work, we propose to learn from learning curves of architectures trained on other datasets to improve the prediction accuracy for novel datasets. On five different image classification benchmarks, we empirically demonstrate that both of our orthogonal contributions independently lead to an acceleration, without any significant loss in accuracy.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Tejaswini Pedapati
  2. Ambrish Rawat
  3. Martin Wistuba
  4. Marco F. Huber
  5. Marc-André Zöller

In Computer Vision and Pattern Recognition

  1. Tejaswini Pedapati
  2. Ambrish Rawat
  3. Martin Wistuba

In Electrical and Electronic Engineering

  1. Tejaswini Pedapati
  2. Ambrish Rawat
  3. Martin Wistuba

Citations in the corpus