In this article, I will be sharing my takeaways from reading Background Matting: The World is Your Green Screen by Sengupta et.al.

Background Matting: The World is Your Green Screen (Sengupta et.al.)

Visual effects in movies make use of a green screen to superimpose computer-generated landscapes into the background. That’s green-screen matting.


Writing this article after reading Fixing the train-test resolution discrepancy (Touvron et. al). I will include the relevant sections of the paper in brackets. I have also used images from the paper.

The current best practice for image preprocessing to train image classifiers looks a little something like this: (Section 1)

train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])

We take different image preprocessings steps at train and test time. And here’s why. …


Greetings! In this article, I will be consolidating my thoughts on Jeremy Howard’s implementation of Kaiming Init from fastai’s Lesson 9. When I encountered this implementation, I had a few troubles in my understanding:

  • How does the code relate to the math from the paper?
  • What is gain(a)?
  • Why is he multiplying std by √3 to get bound?
  • How does he derive fan_in and fan_out?

Code vs Paper

The code for Kaiming Init in the fully connected layer (as presented in Lesson 8) is very different from that in the convolutional layer. I guess this was what threw me off a bit.

#…

gordonlim

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