In machine learning, a question of great interest is understanding what examples are challenging for a model to classify. Identifying atypical examples ensures the safe deployment of models, isolates samples that require further human inspection and provides interpretability into model behavior. In this work, we propose Variance of Gradients (VoG) as a valuable and efficient metric to rank data by difficulty and to surface a tractable subset of the most challenging examples for human-in-the-loop auditing. We show that data points with high VoG scores are far more difficult for the model to learn and over-index on corrupted or memorized examples. Further, restricting the evaluation to the test set instances with the lowest VoG improves the model's generalization performance. Finally, we show that VoG is a valuable and efficient ranking for out-of-distribution detection.
翻译:在机器学习中,一个非常令人感兴趣的问题是了解哪些实例对模型分类具有挑战性。确定非典型实例可以确保模型的安全部署,分离需要人类进一步检查的样本,并为模型行为提供解释性。在这项工作中,我们建议“渐变”是一个宝贵而有效的衡量标准,可以按困难程度对数据进行分类,并展示一个最具有挑战性的例子,供在网上进行人类审计。我们表明,高VoG分数的数据点对于模型来说,要了解腐败或记忆化实例和过多指数要困难得多。此外,将评价限制在最低VoG的测试中,可以改进模型的概括性表现。最后,我们表明,VoG是用于外分配检测的宝贵而有效的排名。