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When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations

A preprint version of the article is available at arXiv.

Abstract

Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent studies have suggested that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category–viewpoint combinations, that is, combinations not seen during training. Here we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and three-dimensional viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization. We show that increasing the number of in-distribution combinations (data diversity) substantially improves generalization to OOD combinations, even with the same amount of training data. We compare learning category and viewpoint in separate and shared network architectures, and observe starkly different trends on in-distribution and OOD combinations, that is, while shared networks are helpful in distribution, separate networks significantly outperform shared ones at OOD combinations. Finally, we demonstrate that such OOD generalization is facilitated by the neural mechanism of specialization, that is, the emergence of two types of neuron—neurons selective to category and invariant to viewpoint, and vice versa.

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Fig. 1: Category–viewpoint datasets.
Fig. 2: Architectures for category recognition and viewpoint estimation.
Fig. 3: Generalization performance for Shared and Separate ResNet-18 as in-distribution combinations are increased for all datasets.
Fig. 4: Generalization performance for different architectures and backbones as in-distribution combinations are increased for iLab and Biased-Cars datasets.
Fig. 5: Specialization to category recognition and viewpoint estimation.
Fig. 6: Neuron specialization (selectivity to category and invariance to viewpoint, and vice versa) in the Biased-Cars dataset.

Data availability

To access and cite the Biased-Cars dataset, please visit https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/F1NQ3R&faces-redirect=true.

Code availability

Source code and demos are available on GitHub at https://github.com/Spandan-Madan/generalization_to_OOD_category_viewpoint_combinations.

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Acknowledgements

We are grateful to T. Poggio and P. Sinha for their insightful advice and warm encouragement. This work has been partially supported by NSF grant IIS-1901030, a Google Faculty Research Award, the Toyota Research Institute, the Center for Brains, Minds and Machines (funded by NSF STC award CCF-1231216), Fujitsu Laboratories (contract no. 40008819) and the MIT-Sensetime Alliance on Artificial Intelligence. We also thank K. Gupta for help with the figures, and P. Sharma for insightful discussions.

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S.M., T.H., J.D. and X.B. conceived, designed and implemented the experiments and carried out the analysis, with contributions from T.S., F.D. and H.P.; S.M., H.H., N.B. and F.D. designed and implemented the Biased-Cars dataset; S.M., T.S. and X.B. wrote the manuscript with contributions from F.D. and H.P.; T.S., F.D., H.P. and X.B. supervised the study.

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Correspondence to Spandan Madan or Xavier Boix.

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This study received funding from Fujitsu Laboratories. The funder through T.S. was involved in conception of the experiment, writing this article and supervising the study. All other authors declare no competing interests.

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Madan, S., Henry, T., Dozier, J. et al. When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nat Mach Intell 4, 146–153 (2022). https://doi.org/10.1038/s42256-021-00437-5

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