Model Compression Using Optimal Transport

Model compression methods are important to allow for easier deployment of deep learning models in compute, memory and energy-constrained environments such as mobile phones. Knowledge distillation is a class of model compression algorithms where knowledge from a large teacher network is transferred t...

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Veröffentlicht in:Proceedings / IEEE Workshop on Applications of Computer Vision S. 3645 - 3654
Hauptverfasser: Lohit, Suhas, Jones, Michael
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2022
Schlagworte:
Computational modeling
Computer vision
Deep learning
Deep Learning -> Efficient Training and Inference Methods for Networks Deep Learning; Object Detection/Recognition/Categorization; Statistical Methods; Learning and Optimization
Image coding
Knowledge engineering
Mobile handsets
Training
ISSN:2642-9381
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Zusammenfassung:Model compression methods are important to allow for easier deployment of deep learning models in compute, memory and energy-constrained environments such as mobile phones. Knowledge distillation is a class of model compression algorithms where knowledge from a large teacher network is transferred to a smaller student network thereby improving the student's performance. In this paper, we show how optimal transport-based loss functions can be used for training a student network which encourages learning student network parameters that help bring the distribution of student features closer to that of the teacher features. We present image classification results on CIFAR-100, SVHN and ImageNet and show that the proposed optimal transport loss functions perform comparably to or better than other loss functions.
ISSN:2642-9381
DOI:10.1109/WACV51458.2022.00370