Understanding EfficientNets and the future of CNN architectures
So far in our exploration from LeNet to DenseNet, we have noticed an underlying theme in the advancement of CNN architectures. That theme is the expansion or scaling of the CNN model through one of the following:
- An increase in the number of layers
- An increase in the number of feature maps or channels in a convolutional layer
- An increase in the spatial dimension going from 32x32 pixel images in LeNet to 224x224 pixel images in AlexNet and so on
These three different aspects on which scaling can be performed are identified as depth, width, and resolution, respectively. Instead of manually scaling these attributes, which often leads to suboptimal results, EfficientNets use neural architecture search to calculate the optimal scaling factors for each of them.
Scaling up depth is deemed important because the deeper the network, the more complex the model, and hence it can learn highly complex features...
