Selective classification using a robust meta-learning approach
- URL: http://arxiv.org/abs/2212.05987v2
- Date: Tue, 2 Jan 2024 20:03:19 GMT
- Title: Selective classification using a robust meta-learning approach
- Authors: Nishant Jain, Karthikeyan Shanmugam and Pradeep Shenoy
- Abstract summary: We propose a novel instance-conditioned reweighting approach that captures predictive uncertainty using an auxiliary network.
We show in controlled experiments that we effectively capture the diverse specific notions of uncertainty through this meta-objective.
For diabetic retinopathy, we see upto 3.4%/3.3% accuracy and AUC gains over SOTA in selective classification.
- Score: 28.460912135533988
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Predictive uncertainty-a model's self awareness regarding its accuracy on an
input-is key for both building robust models via training interventions and for
test-time applications such as selective classification. We propose a novel
instance-conditioned reweighting approach that captures predictive uncertainty
using an auxiliary network and unifies these train- and test-time applications.
The auxiliary network is trained using a meta-objective in a bilevel
optimization framework. A key contribution of our proposal is the
meta-objective of minimizing the dropout variance, an approximation of Bayesian
Predictive uncertainty. We show in controlled experiments that we effectively
capture the diverse specific notions of uncertainty through this
meta-objective, while previous approaches only capture certain aspects. These
results translate to significant gains in real-world settings-selective
classification, label noise, domain adaptation, calibration-and across
datasets-Imagenet, Cifar100, diabetic retinopathy, Camelyon, WILDs,
Imagenet-C,-A,-R, Clothing1M, etc. For Diabetic Retinopathy, we see upto
3.4%/3.3% accuracy and AUC gains over SOTA in selective classification. We also
improve upon large-scale pretrained models such as PLEX.
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