Abstract: Large-scale vision benchmarks have driven---and often even defined---progress in machine learning. However, these benchmarks are merely proxies for the real-world tasks we actually care about. How well do our benchmarks capture such tasks?
In this talk, I will discuss the alignment between our benchmark-driven ML paradigm and the real-world uses cases that motivate it. First, we will explore examples of biases in the ImageNet dataset, and how state-of-the-art models exploit them. We will then demonstrate how these biases arise as a result of design choices in the data collection and curation processes.
Throughout, we illustrate how one can leverage relatively standard tools (e.g., crowdsourcing, image processing) to quantify the biases that we observe.