Name
Abstract | ||
|---|---|---|
To address the high computational and memory cost in 3-D volumetric convolutional neural networks (CNNs), we propose an approach to train binary volumetric CNNs for 3-D object recognition. Our method is specifically designed for 3-D data, in which it transforms the inputs and weights in convolutional/fully connected layers to binary values, which can potentially accelerate the networks by efficien... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIM.2018.2840598 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Object recognition,Shape,Performance evaluation,Solid modeling,Task analysis,Convolutional neural networks,Acceleration | CAD,Bitwise operation,Pattern recognition,Convolutional neural network,Computer Aided Design,Electronic engineering,Ranging,Artificial intelligence,Solid modeling,Mathematics,Binary number,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
68 | 1 | 0018-9456 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Ma | 1 | 85 | 27.49 |
| Yulan Guo | 2 | 672 | 50.74 |
| Yinjie Lei | 3 | 170 | 14.66 |
| wei an | 4 | 62 | 17.06 |