当前位置: X-MOL 学术Front. Comput. Neurosci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Non-uniqueness Phenomenon of Object Representation in Modeling IT Cortex by Deep Convolutional Neural Network (DCNN)
Frontiers in Computational Neuroscience ( IF 3.2 ) Pub Date : 2020-05-12 , DOI: 10.3389/fncom.2020.00035
Qiulei Dong 1, 2, 3 , Bo Liu 1, 2 , Zhanyi Hu 1, 2, 3
Affiliation  

Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modeling approach for neural object representation in primate inferotemporal cortex. In this work, we show that some inherent non-uniqueness problem exists in the DCNN-based modeling of image object representations. This non-uniqueness phenomenon reveals to some extent the theoretical limitation of this general modeling approach, and invites due attention to be taken in practice.

中文翻译:

用深度卷积神经网络 (DCNN) 建模 IT Cortex 中对象表示的非唯一性现象

最近,DCNN(深度卷积神经网络)被认为是灵长类动物颞下皮层神经对象表示的通用且有前途的建模方法。在这项工作中,我们表明在基于 DCNN 的图像对象表示建模中存在一些固有的非唯一性问题。这种非唯一性现象在一定程度上揭示了这种通用建模方法的理论局限性,值得在实践中给予应有的重视。
更新日期:2020-05-12
down
wechat
bug