Towards a More Rigorous Science of Blindspot Discovery in Image Models

Gregory Plumb, Nari Johnson, Angel Cabrera, Ameet Talwalkar

Abstract: A growing body of work studies Blindspot Discovery Methods (BDMs): methods for finding semantically meaningful subsets of the data where an image classifier performs significantly worse, without making strong assumptions. Motivated by observed gaps in prior work, we introduce a new framework for evaluating BDMs, SpotCheck, that uses synthetic image datasets to train models with known blindspots and a new BDM, PlaneSpot, that uses a 2D image representation. We use SpotCheck to run controlled experiments that identify factors that influence BDM performance (e.g., the number of blindspot in a model) and show that PlaneSpot outperforms existing BDMs. Importantly, we validate these findings using real data. Overall, we hope that the methodology and analyses presented in this work will serve as a guide for future work on blindspot discovery.

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