preprint · arXiv (Cornell University) · 2016
Generalization Error of Invariant Classifiers
Jure Sokolić, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues
Cited by 19Open access
Abstract
This paper studies the generalization error of invariant classifiers. In particular, we consider the common scenario where the classification task is invariant to certain transformations of the input, and that the classifier is constructed (or learned) to be invariant to these transformations. Our approach relies on factoring the input space into a product of a base space and a set of transformations. We show that whereas the generalization error of a non-invariant classifier is proportional to the complexity of the input space, the generalization error of an invariant classifier is proportional to the complexity of the base space. We also derive a set of sufficient conditions on the geometry of the base space and the set of transformations that ensure that the complexity of the base space is much smaller than the complexity of the input space. Our analysis applies to general classifiers such as convolutional neural networks. We demonstrate the implications of the developed theory for such classifiers with experiments on the MNIST and CIFAR-10 datasets.
Cite this paper
Sokolić, J., Giryes, R., Sapiro, G., & Rodrigues, M. R. D. (2016). Generalization Error of Invariant Classifiers. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1610.04574
Sokolić, Jure, et al. “Generalization Error of Invariant Classifiers.” arXiv (Cornell University), 2016, https://doi.org/10.48550/arxiv.1610.04574.
Sokolić, Jure, Raja Giryes, Guillermo Sapiro, and Miguel R. D. Rodrigues. 2016. “Generalization Error of Invariant Classifiers.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1610.04574.
Sokolić, J. et al. (2016) ‘Generalization Error of Invariant Classifiers’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1610.04574.
@misc{sokolic2016generalization,
title = {{Generalization Error of Invariant Classifiers}},
author = {Sokolić, Jure and Giryes, Raja and Sapiro, Guillermo and Rodrigues, Miguel R. D.},
journal = {arXiv (Cornell University)},
year = {2016},
doi = {10.48550/arxiv.1610.04574}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
Related papers
Machine Learning Could be Easier if All Data Were MNIST
Toshitaka Hayashi, Dalibor Cimr, Richard Cimler · Frontiers in artificial intelligence and applications · 2023
Cited by 0
Training on test data: Removing near duplicates in Fashion-MNIST
Christopher Geier · arXiv (Cornell University) · 2019
Cited by 4Open access
Symmetric Rectified Linear Units for Fully Connected Deep Models
He Hu · Lecture notes in computer science · 2018
Cited by 5
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, Han, Kashif Rasul, Roland Vollgraf · arXiv (Cornell University) · 2017
Cited by 10,564Open access
РЕЗУЛЬТАТИ НАЛАШТУВАННЯ ПАРАМЕТРІВ НЕЙРОННИХ ГЛИБОКИХ МЕРЕЖ ЩОДО РОЗПІЗНАВАННЯ FASHION MNIST DATASET
Viktoriia Hnatushenko, Tatyana Fenenko, О.Л. ДОРОШ · Applied Questions of Mathematical Modeling · 2023
Cited by 0Open access
Deeper into Image Classification
Jatin Bindra, Bulla Rajesh, Savita Ahlawat · Advances in intelligent systems and computing · 2020
Cited by 2
Analysis of deep learning-based deep clustering method
육군사관학교 AI·데이터과학과 부교수, Hyun Kwon, Jun Ho Lee · Jouranl of Information and Security · 2023
Cited by 0
evt_MNIST: A spike based version of traditional MNIST an event-based MNIST
Mazdak Fatahi, Mahyar Shahsavari, Mahmood Ahmadi et al. · LillOA (Université de Lille (University Of Lille)) · 2016
Cited by 3Open access