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DeLightCMU / RSC

This is the official implementation of Self-Challenging Improves Cross-Domain Generalization, ECCV2020
BSD 2-Clause "Simplified" License
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Question about the spatial-wise RSC implementation #19

Closed AhmedFrikha closed 3 years ago

AhmedFrikha commented 3 years ago

Can you please elaborate more on the question (1) ? I still don't understand why the spatial-wise RSC was implemented as a sum of the activations (x_new) weighted by the mean gradients of each channel (spatial_mean = torch.sum(x_new * grad_channel_mean, 1)).

Based on the description in the paper ("global average pooling is applied along the channel dimension to the gradient tensor G to produce a weighting matrix wi of size [7 × 7]."), instead of lines 99-103 it should be just: spatial_mean = torch.mean(grads_val.view(num_rois, num_channel, -1), dim=1).

Originally posted by @AhmedFrikha in https://github.com/DeLightCMU/RSC/issues/15#issuecomment-796242412

Justinhzy commented 3 years ago

Thanks for your questions. Different from other network architectures, ResNet is a special case that uses the average pool at the last conv layer. We use the current implementation for convenience so that all networks can share the same implementation. Feel free to use your implementation or downsample spatial middle layer gradient as guidance.