Convolutional Neural Networks
Inception v2
Introduced by Ioffe et al. in Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftInception v2 is the second generation of Inception convolutional neural network architectures which notably uses batch normalization. Other changes include dropping dropout and removing local response normalization, due to the benefits of batch normalization.
Source: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 5 | 29.41% |
| Autonomous Driving | 3 | 17.65% |
| Image Classification | 2 | 11.76% |
| Anomaly Detection | 1 | 5.88% |
| Supervised Anomaly Detection | 1 | 5.88% |
| 2D Cyclist Detection | 1 | 5.88% |
| Traffic Sign Detection | 1 | 5.88% |
| Autonomous Vehicles | 1 | 5.88% |
| Video Prediction | 1 | 5.88% |
Auxiliary Classifier
Batch Normalization
Dense Connections
Inception Module
Max Pooling
ReLU
Softmax
