Overview - 何がすごい?
We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results. Errors in test sets are numerous and widespread: we estimate an average of 3.4% errors across the 10 datasets, where for example 2916 label errors comprise 6% of the ImageNet validation set. Putative label errors are identified using confident learning algorithms and then human-validated via crowdsourcing (54% of the algorithmically-flagged candidates are indeed erroneously labeled). Traditionally, machine learning practitioners choose which model to deploy based on test accuracy - our findings advise caution here, proposing that judging models over correctly labeled test sets may be more useful, especially for noisy real-world datasets. Surprisingly, we find that lower capacity models may be practically more useful than higher capacity models in real-world datasets with high proportions of erroneously labeled data. For example, on ImageNet with corrected labels: ResNet-18 outperforms ResNet50 if the prevalence of originally mislabeled test examples increases by just 6%. On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by just 5%.
- Confident Learningと言われる手法で間違っていそうな画像を探す → Amazon Mechanical Turkでクラウドソーシングで複数の人間に確認してもらう
Pervasive Label Errors in ML Datasets Destabilize Benchmarks
To our surprise, label errors are pervasive across 10 popular benchmark test sets used in most machine learning research, destabilizing benchmarks. It's well-known that ML datasets are not perfectly labeled. But there hasn't been much research to systematically quantify how error-ridden the most commonly-used ML datasets are at scale.