CORnet-S

doi:doi:10.57702/etxbtug1

CORnet-S

A shallow recurrent model that follows neuroanatomy more closely than standard machine learning ANNs, and is among the top models on Brain-Score yet remains competitive on ImageNet and on transfer tests to CIFAR-100.

BibTex:

@dataset{Jonas_Kubilius_and_Martin_Schrimpf_and_Kohitij_Kar_and_Rishi_Rajalingham_and_Elias_B_Issa_and_Pouya_Bashivan_and_Jonathan_Prescott-Roy_and_Kailyn_Schmidt_and_Aran_Nayebi_and_Daniel_Bear_and_Daniel_LK_Yamins_and_James_J_DiCarlo_2024,
    abstract = {A shallow recurrent model that follows neuroanatomy more closely than standard machine learning ANNs, and is among the top models on Brain-Score yet remains competitive on ImageNet and on transfer tests to CIFAR-100.},
    author = {Jonas Kubilius and Martin Schrimpf and Kohitij Kar and Rishi Rajalingham and Elias B Issa and Pouya Bashivan and Jonathan Prescott-Roy and Kailyn Schmidt and Aran Nayebi and Daniel Bear and Daniel LK Yamins and James J DiCarlo},
    doi = {10.57702/etxbtug1},
    institution = {No Organization},
    keyword = {'brain-computer interfaces', 'computer vision', 'neural networks', 'recurrent neural networks', 'shallow models'},
    month = {dec},
    publisher = {TIB},
    title = {CORnet-S},
    url = {https://service.tib.eu/ldmservice/dataset/cornet-s},
    year = {2024}
}