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- W4311997017 abstract "In this paper, we provide 20,000 non-trivial human annotations on popular datasets as a first step to bridge gap to studying how natural semantic spurious features affect image classification, as prior works often study datasets mixing low-level features due to limitations in accessing realistic datasets. We investigate how natural background colors play a role as spurious features by annotating the test sets of CIFAR10 and CIFAR100 into subgroups based on the background color of each image. We name our datasets textbf{CIFAR10-B} and textbf{CIFAR100-B} and integrate them with CIFAR-Cs. We find that overall human-level accuracy does not guarantee consistent subgroup performances, and the phenomenon remains even on models pre-trained on ImageNet or after data augmentation (DA). To alleviate this issue, we propose textbf{FlowAug}, a emph{semantic} DA that leverages decoupled semantic representations captured by a pre-trained generative flow. Experimental results show that FlowAug achieves more consistent subgroup results than other types of DA methods on CIFAR10/100 and on CIFAR10/100-C. Additionally, it shows better generalization performance. Furthermore, we propose a generic metric, emph{MacroStd}, for studying model robustness to spurious correlations, where we take a macro average on the weighted standard deviations across different classes. We show textit{MacroStd} being more predictive of better performances; per our metric, FlowAug demonstrates improvements on subgroup discrepancy. Although this metric is proposed to study our curated datasets, it applies to all datasets that have subgroups or subclasses. Lastly, we also show superior out-of-distribution results on CIFAR10.1." @default.
- W4311997017 created "2023-01-03" @default.
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- W4311997017 date "2022-12-16" @default.
- W4311997017 modified "2023-09-27" @default.
- W4311997017 title "Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification" @default.
- W4311997017 doi "https://doi.org/10.48550/arxiv.2212.08649" @default.
- W4311997017 hasPublicationYear "2022" @default.
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