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Do We Train on Test Data? Purging CIFAR of Near-Duplicates
The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked datasets in computer vision and are often used to evaluate novel methods and model architectures in the field of deep learning. However, we find that 3.3% and 10% of the images from the test sets of these datasets have dupli...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321059/ https://www.ncbi.nlm.nih.gov/pubmed/34460587 http://dx.doi.org/10.3390/jimaging6060041 |