A novel neuroscience-inspired architecture: for computer vision applications
The theory behind deep learning, the human visual system was investigated and general principles of how it functions are extracted. Our finding is that there are neuroscience theories that are not utilized in deep learning. Therefore, in this work, a novel model utilizing some of those theories...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronic Engineers
2016
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/50466/ http://irep.iium.edu.my/50466/ http://irep.iium.edu.my/50466/ http://irep.iium.edu.my/50466/1/50466_A_novel_neuroscience-inspired_architecture.pdf http://irep.iium.edu.my/50466/4/50466_A%20novel%20neuroscience_scopus.pdf |
| Summary: | The theory behind deep learning, the human visual
system was investigated and general principles of how it functions
are extracted. Our finding is that there are neuroscience theories that
are not utilized in deep learning. Therefore, in this work, a novel
model utilizing some of those theories is developed. The new model
addresses the parallel nature of the human brain compared to the
hierarchal (serial) brain model that is followed by current deep
learning systems. The validation of the proposed model was
conducted using “Shape” feature dimension. The results show up to
2% accuracy rate compared to our implementation of DeepFace, a
high performing face recognition algorithm that was developed by
Facebook, is achieved under the same hardware/ software conditions;
and we were able to speed up the training up to 21% per a training
patch compared to DeepFace. |
|---|