Knowledge distillation: A good teacher is patient and consistent

There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. In this paper we address this issue and significantly bridge the gap between these two types of models. Throughout our empi...

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Bibliographic Details
Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 10915 - 10924
Main Authors: Beyer, Lucas, Zhai, Xiaohua, Royer, Amelie, Markeeva, Larisa, Anil, Rohan, Kolesnikov, Alexander
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2022
Subjects:
Computational modeling
Computer vision
Data models
Deep learning architectures and techniques; Efficient learning and inferences; Machine learning; Optimization methods; Representation learning
Image coding
Manifolds
Schedules
Training
ISSN:1063-6919
Online Access:Get full text
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Summary:There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. In this paper we address this issue and significantly bridge the gap between these two types of models. Throughout our empirical investigation we do not aim to necessarily propose a new method, but strive to identify a robust and effective recipe for making state-of-the-art large scale models affordable in practice. We demonstrate that, when performed correctly, knowledge distillation can be a powerful tool for reducing the size of large models without compromising their performance. In particular, we uncover that there are certain implicit design choices, which may drastically affect the effectiveness of distillation. Our key contribution is the explicit identification of these design choices, which were not previously articulated in the literature. We back up our findings by a comprehensive empirical study, demonstrate compelling results on a wide range of vision datasets and, in particular, obtain a state-of-the-art ResNet-50 model for ImageNet, which achieves 82.8% top-1 accuracy.
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.01065