Unsupervised Domain Adaptation by Backpropagation

Pytorch implementation for the Gradient Reversal Layer in paper "Unsupervised Domain Adaptation by Backpropagation".

@misc{ganin2015unsupervised,
      title={Unsupervised Domain Adaptation by Backpropagation}, 
      author={Yaroslav Ganin and Victor Lempitsky},
      year={2015},
      eprint={1409.7495},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

Gradient Reversal Layer

Gradient Reversal Layer

During the forward propagation, Gradient Reversal Layer(GRL) acts as an identity transform. During the backpropagation though, GRL takes the gradient from the subsequent level, multiplies it by α (or λ in image) and passes it to the preceding layers.

Features on MNIST and SVHN

Usage

As module

from torch import nn
from gradient_reversal import GradientReversal

# define a network with Gradient Reversal Layer
net = nn.Sequential(
      nn.Linear(10, 10),
      GradientReversal(alpha=1.)
)

As function

import torch
from gradient_reversal import revgrad

alpha = torch.tensor([1.])

x = torch.tensor([4.], requires_grad=True)
x_rev = torch.tensor([4.], requires_grad=True)

y = x*5
y = y+6

y_rev = x_rev*5
y_rev = revgrad(y_rev, alpha)
y_rev = y_rev+6

y.backward()
y_rev.backward()

print(f'x gradient: {x.grad}') # 5
print(f'reversed x gradient: {x_rev.grad}') # -5

assert x.grad==-x_rev.grad

References

[1] Yaroslav Ganin, & Victor Lempitsky. (2015). Unsupervised Domain Adaptation by Backpropagation.