Our Evaluation Metric Needs an Update to Encourage Generalization

• URL: http://arxiv.org/abs/2007.06898v1
• Date: Tue, 14 Jul 2020 08:15:19 GMT
• Title: Our Evaluation Metric Needs an Update to Encourage Generalization
• Authors: Swaroop Mishra, Anjana Arunkumar, Chris Bryan and Chitta Baral
• Abstract summary: Models that surpass human performance on popular benchmarks display significant degradation in performance on exposure to Out of Distribution data. We propose a simple and novel evaluation metric, WOOD Score, that encourages generalization during evaluation.
• Score: 24.6240575061124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Models that surpass human performance on several popular benchmarks display significant degradation in performance on exposure to Out of Distribution (OOD) data. Recent research has shown that models overfit to spurious biases and `hack' datasets, in lieu of learning generalizable features like humans. In order to stop the inflation in model performance -- and thus overestimation in AI systems' capabilities -- we propose a simple and novel evaluation metric, WOOD Score, that encourages generalization during evaluation.

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