Jul 12, 2020
Dataset replication is a useful tool for assessing whether models have overfit to a specific validation set or the exact circumstances under which it was generated. In this paper, we highlight the importance of statistical modeling in dataset replication: we present unintuitive yet pervasive ways in which statistical bias, when left unmitigated, can skew results. Specifically, we examine ImageNet-v2, a replication of the ImageNet dataset that induces a significant drop in model accuracy, presumed to be caused by a benign distribution shift between the datasets. We show, however, that by identifying and accounting for the aforementioned bias, we can explain the vast majority of this accuracy drop. We conclude with concrete recommendations for recognizing and avoiding bias in dataset replication.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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