VGG is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.
Image: Davi Frossard
Source: Very Deep Convolutional Networks for Large-Scale Image RecognitionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 60 | 8.82% |
General Classification | 43 | 6.32% |
Super-Resolution | 36 | 5.29% |
Object Detection | 28 | 4.12% |
Classification | 27 | 3.97% |
Image Super-Resolution | 23 | 3.38% |
Semantic Segmentation | 20 | 2.94% |
Quantization | 18 | 2.65% |
Style Transfer | 17 | 2.50% |
Component | Type |
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Convolution
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Convolutions | |
Dense Connections
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Feedforward Networks | |
Dropout
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Regularization | |
Max Pooling
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Pooling Operations | |
ReLU
|
Activation Functions | |
Softmax
|
Output Functions |