Convolutional Neural Networks
Inception-v4
Introduced by Szegedy et al. in Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningInception-v4 is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than Inception-v3.
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 | 5 | 17.86% |
| Image Classification | 3 | 10.71% |
| Image Segmentation | 2 | 7.14% |
| Semantic Segmentation | 2 | 7.14% |
| Classification | 2 | 7.14% |
| Lesion Segmentation | 2 | 7.14% |
| Meta-Learning | 2 | 7.14% |
| Semi-Supervised Image Classification | 1 | 3.57% |
| Classifier calibration | 1 | 3.57% |
Average Pooling
Dropout
Inception-A
Inception-B
Inception-C
Reduction-A
Reduction-B
Softmax
