Abstract
Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model, in particular how well it matches a domain expert’s understanding of a task. Underspecification [6] refers to the existence of multiple models that are indistinguishable in their in-domain accuracy, even though they differ in other desirable properties such as out-of-distribution (OOD) performance. Identifying these situations is critical for assessing the reliability of ML models. We formalize the concept of underspecification and propose a method to identify and partially address it. We train multiple models with an independence constraint that forces them to implement different functions. They discover predictive features that are otherwise ignored by standard empirical risk minimization (ERM), which we then distill into a global model with superior OOD performance. Importantly, we constrain the models to align with the data manifold to ensure that they discover meaningful features. We demonstrate the method on multiple datasets in computer vision (collages, WILDS-Camelyon17, GQA) and discuss general implications of underspecification. Most notably, in-domain performance cannot serve for OOD model selection without additional assumptions (See https://arxiv.org/abs/2207.02598 for the full-length version of this work).
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Notes
- 1.
In this paper, OOD means there is a covariate shift between training and test data [44].
- 2.
We define f to output logits. A binary prediction \(\hat{y}\) is obtained as \(\hat{y}={\text {round}}\big (\sigma \big (f({\boldsymbol{x}})\big )\big )\).
- 3.
- 4.
In our implementation, masked elements are not replaced with zeros, but rater with random values from other instances in the current mini-batch.
- 5.
We obtain very similar results between fine-tuning and retraining models from scratch on the masked data.
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Teney, D., Peyrard, M., Abbasnejad, E. (2022). Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13683. Springer, Cham. https://doi.org/10.1007/978-3-031-20050-2_27
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