Jul 12, 2020
Creating machine learning datasets often necessitates the use of automated data retrieval and crowdsourced annotation, giving rise to an inevitably noisy pipeline. We perform large-scale human studies to investigate the impact of such a pipeline on ImageNet—one of the key datasets driving progress in computer vision. We find that seemingly innocuous design choices (e.g., exact task setup, filtering procedure, annotators employed) can have an unexpected impact on the resulting dataset—including the introduction of spurious correlations that state-of-the-art models exploit. Overall, our results highlight a misalignment between the way we train our models and the task we actually expect them to solve, emphasizing the need for fine-grained evaluation techniques that go beyond average-case accuracy.
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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