Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task

The competition "Predicting Generalization in Deep Learning (PGDL)" aims to provide a platform for rigorous study of generalization of deep learning models and offer insight into the progress of understanding and explaining these models. This report presents the solution that was submitted...

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Bibliographic Details
Published in:arXiv.org 2020-12
Main Authors: Mežnar, Sebastian, Škrlj, Blaž
Format: Article
Language:English
Subjects:
Competition
Computer architecture
Deep learning
Neural networks
Online Access:Get full text
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Summary:The competition "Predicting Generalization in Deep Learning (PGDL)" aims to provide a platform for rigorous study of generalization of deep learning models and offer insight into the progress of understanding and explaining these models. This report presents the solution that was submitted by the user \emph{smeznar} which achieved the eight place in the competition. In the proposed approach, we create simple metrics and find their best combination with automatic testing on the provided dataset, exploring how combinations of various properties of the input neural network architectures can be used for the prediction of their generalization.
ISSN:2331-8422