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- W3167964457 abstract "Machine learning models often use spurious patterns such as relying on presence of a person to detect a tennis racket, which do not generalize. In this work, we present an end-to-end pipeline for identifying and mitigating spurious patterns for image classifiers. We start by finding patterns such as the model's prediction for tennis racket changes 63% of time if we hide people. Then, if a pattern is spurious, we mitigate it via a novel form of data augmentation. We demonstrate that this approach identifies a diverse set of spurious patterns and that it mitigates them by producing a model that is both more accurate on a distribution where spurious pattern is not helpful and more robust to distribution shift." @default.
- W3167964457 created "2021-06-22" @default.
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- W3167964457 date "2021-06-03" @default.
- W3167964457 modified "2023-09-23" @default.
- W3167964457 title "Finding and Fixing Spurious Patterns with Explanations." @default.
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