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Dilated convolutions can be used to improve the context-awareness of CNNs. It would be interesting to compare dilated CNNs to the alternative multi-scale approach of the already supported U-Net-like architectures (example).
Implementing this feature is probably not much work, since it is directly implemented in Theano's convolution operation (depends on #4). This will require theano>=0.9.
I don't think we need to subclass elektronn2.neuromancer.Conv for this, but we can add this functionality directly to the Conv code like they did in Keras (see the dilation_rate parameter of the Conv*d constructors here).
The text was updated successfully, but these errors were encountered:
Dilated convolutions can be used to improve the context-awareness of CNNs. It would be interesting to compare dilated CNNs to the alternative multi-scale approach of the already supported U-Net-like architectures (example).
Implementing this feature is probably not much work, since it is directly implemented in Theano's convolution operation (depends on #4). This will require
theano>=0.9
.I don't think we need to subclass
elektronn2.neuromancer.Conv
for this, but we can add this functionality directly to theConv
code like they did in Keras (see thedilation_rate
parameter of theConv*d
constructors here).The text was updated successfully, but these errors were encountered: