| """DANN gradient reversal (Ganin & Lempitsky, ICML 2015) -- DG baseline.
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|
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| Retired as the ARC-V *contribution* -- a marginal source-adversary optimizes source
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| decodability, which this project showed is uncorrelated with robustness, and it
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| plateaus once the discriminator is fooled (ARCV_METHOD_DESIGN.md §2). Kept as a
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| required baseline. The gradient-reversal layer is the identity on the forward pass
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| and negates (scaled by ``lambda_``) the gradient on the backward pass, so a source
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| discriminator stacked on top pushes the backbone toward source-confusion.
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| """
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| import torch
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|
|
|
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| class _GradReverse(torch.autograd.Function):
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| @staticmethod
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| def forward(ctx, x, lambda_):
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| ctx.lambda_ = float(lambda_)
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| return x.view_as(x)
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|
|
| @staticmethod
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| def backward(ctx, grad_output):
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| return -ctx.lambda_ * grad_output, None
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|
|
|
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| def grad_reverse(x: torch.Tensor, lambda_: float = 1.0) -> torch.Tensor:
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| """Identity forward; gradient is negated and scaled by ``lambda_`` backward."""
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| return _GradReverse.apply(x, lambda_)
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|
|
