Convolutional Neural Networks
Inception-ResNet-v2
Introduced by Szegedy et al. in Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningInception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates residual connections (replacing the filter concatenation stage of the Inception architecture).
Source: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| General Classification | 3 | 9.09% |
| Image Classification | 3 | 9.09% |
| Classification | 2 | 6.06% |
| Machine Translation | 2 | 6.06% |
| Decoder | 2 | 6.06% |
| Breast Cancer Detection | 1 | 3.03% |
| Classifier calibration | 1 | 3.03% |
| Fine-Grained Image Classification | 1 | 3.03% |
| Fine-Grained Image Recognition | 1 | 3.03% |
Average Pooling
Dropout
Inception-ResNet-v2-A
Inception-ResNet-v2-B
Inception-ResNet-v2-C
Inception-ResNet-v2 Reduction-B
Reduction-A
Softmax
