More on Deconvolutionjeudi 05 juillet 2018
I wrote about this paper before, but I am going to again because it has been so enormously useful to me. I am still working on segmentation of mammograms to highlight abnormalities and I recently decided to scrap the approach I had been taking to upsampling the image and start that part from scratch.
When I started I had been using the earliest approach to upsampling, which basically was take my classifier, remove the last fully-connected layer and upsample that back to full resolution with transpose convolutions. This worked well enough, but the network had to upsample images from 2x2x1024 to 640x640x2 and in order to do this I needed to add skip connections from the downsizing section to the upsampling section. This caused problems because the network would add features of the input image to the output, regardless of whether the features were relevant to the label. I tried to get around this by adding bottleneck layers before the skip connection in order to only select the pertinent features, but this greatly slowed down training and didn't help much and the output ended up with a lot of weird artifacts.
In "Deconvolution and Checkerboard Artifacts", Odena et al. have demonstrated that replacing transpose convolutions with nearest neighbors resizing produces smoother images than using transpose convolutions. I tried replacing a few of my tranpose convolutions with resizes and the results improved.
Then I started reading about dilated convolutions and I started wondering why I was downsizing my input from 640x640 to 5x5 just to have to resize it back up. I removed all the fully-connected layers (which in fact were 1x1 convolutions rather than fully-connected layers) and then replaced the last max pool with a dilated convolution.
I replaced all of the transpose convolutions with resizes, except for the last two layers, as suggested by Odena et al, and the final tranpose convolution has a stride of 1 in order to smooth out artifacts.
In the downsizing section, the current model reduces the input from 640x640x1 to 20x20x512, then it is upsampled by using nearest neighbors resizing followed by plain convolutions to 320x320x32. Finally there is a tranpose convolution with a stride of 2 followed by a transpose convolution with a stride of 1 and then a softmax for the output. As an added bonus, this version of the model trains significantly faster than upsampling with transpose convolutions.
I just started training this model, but I am fairly confident it will perform better than previous upsampling schemes as when I extracted the last downsizing convolutional layer from the model that layer appeared closer to the label (although much smaller) than the final output did. I will update when I have actual results.
Update - After training the model for just one epoch, with the downsizing layer weights initialized from a previous model, the results are already significantly better than under the previous scheme.