Exploring Alignment of Representations with Human Perception
- URL: http://arxiv.org/abs/2111.14726v1
- Date: Mon, 29 Nov 2021 17:26:50 GMT
- Title: Exploring Alignment of Representations with Human Perception
- Authors: Vedant Nanda and Ayan Majumdar and Camila Kolling and John P.
Dickerson and Krishna P. Gummadi and Bradley C. Love and Adrian Weller
- Abstract summary: We show that inputs that are mapped to similar representations by the model should be perceived similarly by humans.
Our approach yields a measure of the extent to which a model is aligned with human perception.
We find that various properties of a model like its architecture, training paradigm, training loss, and data augmentation play a significant role in learning representations that are aligned with human perception.
- Score: 47.53970721813083
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We argue that a valuable perspective on when a model learns \textit{good}
representations is that inputs that are mapped to similar representations by
the model should be perceived similarly by humans. We use
\textit{representation inversion} to generate multiple inputs that map to the
same model representation, then quantify the perceptual similarity of these
inputs via human surveys. Our approach yields a measure of the extent to which
a model is aligned with human perception. Using this measure of alignment, we
evaluate models trained with various learning paradigms (\eg~supervised and
self-supervised learning) and different training losses (standard and robust
training). Our results suggest that the alignment of representations with human
perception provides useful additional insights into the qualities of a model.
For example, we find that alignment with human perception can be used as a
measure of trust in a model's prediction on inputs where different models have
conflicting outputs. We also find that various properties of a model like its
architecture, training paradigm, training loss, and data augmentation play a
significant role in learning representations that are aligned with human
perception.
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