A deep-learning model for predictive archaeology and archaeological community detection
Abstract Deep learning is a powerful tool for exploring large datasets and discovering new patterns. This work presents an account of a metric learning-based deep convolutional neural network (CNN) applied to an archaeological dataset. The proposed account speaks of three stages: training, testing/v...
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Autores principales: | Abraham Resler, Reuven Yeshurun, Filipe Natalio, Raja Giryes |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Springer Nature
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/9d943c62d5c046279d89872e62d1ab2c |
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