The Spotlight: A General Method for Discovering Systematic Errors in
Deep Learning Models
- URL: http://arxiv.org/abs/2107.00758v1
- Date: Thu, 1 Jul 2021 21:58:00 GMT
- Title: The Spotlight: A General Method for Discovering Systematic Errors in
Deep Learning Models
- Authors: Greg d'Eon, Jason d'Eon, James R. Wright, Kevin Leyton-Brown
- Abstract summary: This paper introduces a method for discovering systematic errors, which we call the spotlight.
Similar inputs tend to have similar representations in the final hidden layer of a neural network.
We leverage this structure by "shining a spotlight" on this representation space to find contiguous regions where the model performs poorly.
- Score: 18.209010694469647
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Supervised learning models often make systematic errors on rare subsets of
the data. However, such systematic errors can be difficult to identify, as
model performance can only be broken down across sensitive groups when these
groups are known and explicitly labelled. This paper introduces a method for
discovering systematic errors, which we call the spotlight. The key idea is
that similar inputs tend to have similar representations in the final hidden
layer of a neural network. We leverage this structure by "shining a spotlight"
on this representation space to find contiguous regions where the model
performs poorly. We show that the spotlight surfaces semantically meaningful
areas of weakness in a wide variety of model architectures, including image
classifiers, language models, and recommender systems.
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