This code was tested using Python 3.6 and Tensorflow 2.0 All the dependencies are in the requirement.txt file
As a baseline, I implemented the WCNN described in the paper.
Two types of normalization are tested for the recurrent convolution layer : batch normalization and local response normalization. In the experiments that I've done, I used a fixed learning rate, dropout and batch normalization.
I trained the networks for 100 epochs.
In the visual cortex, recurrent conenctions are playing a major role in learning. There are type types of recurrent connections : local recurrent conenctions and long range recurrent connections. In ResNet, lon range connections are added and skip connections show great impact on the performance. RCNN is introducing local recurrent connections. A recent paper added both types of recurrent conenctions to a feedforward CNN to mimic the dynamic of visual system.
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