Papers › Augmenting Deep Classifiers with Polynomial Neural Networks

Augmenting Deep Classifiers with Polynomial Neural Networks

16 Apr 2021arXiv:2104.07916archive 2025-07-28

Grigorios G Chrysos, Markos Georgopoulos, Jiankang Deng, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar

Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been achieved by a variety of recently proposed architectures, the majority of which are seemingly disconnected. In this work, we cast the study of deep classifiers under a unifying framework. In particular, we express state-of-the-art architectures (e.g., residual and non-local networks) in the form of different degree polynomials of the input. Our framework provides insights on the inductive biases of each model and enables natural extensions building upon their polynomial nature. The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks. The expressivity of the proposed models is highlighted both in terms of increased model performance as well as model compression. Lastly, the extensions allowed by this taxonomy showcase benefits in the presence of limited data and long-tailed data distributions. We expect this taxonomy to provide links between existing domain-specific architectures. The source code is available at \url{https://github.com/grigorisg9gr/polynomials-for-augmenting-NNs}.

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grigorisg9gr/polynomials-for-augmenting-nns officialmentioned in papermentioned on GitHubpytorch report
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Tasks

Audio ClassificationGeneral ClassificationImage ClassificationModel CompressionObject DetectionObject SegmentationPolynomial Neural Networks

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Audio Classification Speech Commands PDC Accuracy 97.8 #3 of 7 Archive leaderboard report
Image Classification CIFAR-100 PDC Percentage correct 77.9 #140 of 211 Archive leaderboard report
Image Classification ImageNet PDC Number of params 11.51M #1007 of 1060 Archive leaderboard report
Image Classification ImageNet PDC Top 1 Accuracy 71.6% #1007 of 1060 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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