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

• URL: http://arxiv.org/abs/2012.09117v1
• Date: Wed, 16 Dec 2020 17:59:13 GMT
• Title: Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task
• Authors: Sebastian Me\v{z}nar and Bla\v{z} \v{S}krlj
• Abstract summary: The report presents the solution that was submitted by the user emphsmeznar 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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