mahdihosseini/GenProb
In Search of Probeable Generalization Measures [ICMLA2021]
This project helps machine learning researchers and practitioners evaluate and compare different ways to measure how well a deep learning model will perform on new, unseen data. It takes trained deep convolutional neural networks (CNNs) and calculates various 'generalization measures' from their internal layer weights. The output is a CSV file containing these metric evaluations on a layer-by-layer basis, helping users understand and improve their model's generalization capabilities.
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Use this if you are a machine learning researcher or practitioner who needs to quantitatively assess and compare different metrics for predicting deep learning model generalization performance early in the training process.
Not ideal if you are looking for a tool to train new deep learning models or optimize hyperparameters without a specific focus on evaluating generalization measures.
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Jupyter Notebook
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MIT
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Last pushed
Jan 04, 2022
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