Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex with a deep self-supervised generative model, β-VAE,...
Auteurs principaux: | Higgins, I, Chang, L, Langston, V, Hassabis, D, Summerfield, C, Tsao, D, Botvinick, M |
---|---|
Format: | Journal article |
Langue: | English |
Publié: |
Springer Nature
2021
|
Documents similaires
-
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
par: Irina Higgins, et autres
Publié: (2021-11-01) -
Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons
par: Baldassi, Carlo, et autres
Publié: (2013) -
Visual identification following inferotemporal ablation in the monkey.
par: Gaffan, D, et autres
Publié: (1986) -
Disentangling the latent space of GANs for semantic face editing
par: Yongjie Niu, et autres
Publié: (2023-01-01) -
Disentangling the latent space of GANs for semantic face editing.
par: Yongjie Niu, et autres
Publié: (2023-01-01)