Saltar al contenido principal

ETHNOS_APP

Inicio • Búsqueda • Revistas • Lista 0

Connor Shorten

Datos Biográficos

ID10713499
NOMBREConnor Shorten
NOMBRESConnor
APELLIDOShorten
FIRMASHORTEN C
AFILIACIONESFlorida Atlantic University
ORCID0000-0001-6253-6861
VERIFICADONo
TOTAL DE OBRAS1
TOTAL DE CITAS0
OBRAS COMO AUTOR1
OBRAS COMO EDITOR0
PRIMER AÑO DE PUBLICACIÓN2019
AÑO MÁS RECIENTE DE PUBLICACIÓN2019
ÍNDICE H0
  • A survey on Image Data Augmentation for Deep Learning

    Connor Shorten, Taghi M Khoshgoftaar

    Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Aug…

Sin obras prominentes en esta página.

  • A survey on Image Data Augmentation for Deep Learning

    Connor Shorten, Taghi M Khoshgoftaar

    Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Aug…

Advanced Neural Network Applications (1 obra) · AI in cancer detection (1 obra) · Artificial Intelligence (1 obra) · Artificial neural network (1 obra) · Benchmark (surveying) (1 obra) · Big data (1 obra) · Computer Science (1 obra) · Convolutional neural network (1 obra) · Data mining (1 obra) · Data science (1 obra)

Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae