Using Early Readouts to Mediate Featural Bias in Distillation
- URL: http://arxiv.org/abs/2310.18590v2
- Date: Wed, 8 Nov 2023 13:13:13 GMT
- Title: Using Early Readouts to Mediate Featural Bias in Distillation
- Authors: Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala, Ganesh
Ramakrishnan, Pradeep Shenoy
- Abstract summary: Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks.
We propose a novel early readout mechanism whereby we attempt to predict the label using representations from earlier network layers.
- Score: 30.5299408494168
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep networks tend to learn spurious feature-label correlations in real-world
supervised learning tasks. This vulnerability is aggravated in distillation,
where a student model may have lesser representational capacity than the
corresponding teacher model. Often, knowledge of specific spurious correlations
is used to reweight instances & rebalance the learning process. We propose a
novel early readout mechanism whereby we attempt to predict the label using
representations from earlier network layers. We show that these early readouts
automatically identify problem instances or groups in the form of confident,
incorrect predictions. Leveraging these signals to modulate the distillation
loss on an instance level allows us to substantially improve not only group
fairness measures across benchmark datasets, but also overall accuracy of the
student model. We also provide secondary analyses that bring insight into the
role of feature learning in supervision and distillation.
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