Cargando…
Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation
Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the...
Autores principales: | Cui, Yibo, Zhang, Chi, Qiao, Kai, Wang, Linyuan, Yan, Bin, Tong, Li |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7564968/ https://www.ncbi.nlm.nih.gov/pubmed/32887405 http://dx.doi.org/10.3390/brainsci10090602 |
Ejemplares similares
-
A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs
por: Yu, Ziya, et al.
Publicado: (2020) -
GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability
por: Cui, Yibo, et al.
Publicado: (2021) -
Category Decoding of Visual Stimuli From Human Brain Activity Using a Bidirectional Recurrent Neural Network to Simulate Bidirectional Information Flows in Human Visual Cortices
por: Qiao, Kai, et al.
Publicado: (2019) -
Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation
por: Xu, Yujia, et al.
Publicado: (2023) -
Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
por: Hu, Weijun, et al.
Publicado: (2019)