Comments (2)
Thanks for experimenting on this! Not really a "reproduction" problem, as we didn't have these results in the paper.
Accuracy may go up or down for different datasets. The shift-invariance should be improved more reliably!
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Hi, I want to train antialias-cnn on cub200 dataset. I use ResNet-50 backbone.
However, I do not see increase of accuracy with BlurPool after several experiments. Do you have any suggestions for antialias-cnn on fine-grained datasets (e.g. cub200)?
Thank you.
Expected Behaviour
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Can you post the performance on the CUB200?
Thanks.
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Related Issues (20)
- Is the blur_kernel trainable or parametric? HOT 1
- RuntimeError when training resnext50_32x4d HOT 2
- can Downsample use tensorflow? HOT 1
- Depthwise convolutions HOT 1
- 3D implementation ? HOT 4
- Larger strides/downsampling factors HOT 2
- About Internal feature distance for shift Equivariance visualization HOT 2
- Increased memory usage vs. torchvision equivalent HOT 3
- ResNet parameter "pool_only=True" HOT 1
- Max-blur-pool used in text recognition model (CRNN) HOT 1
- HTTP Error 403: Forbidden when loading weights HOT 2
- Any plans to explore using sinc filter for downsampling? HOT 1
- Padding size issue for small images HOT 2
- Could you please provide a 3D implementation in pytorch?
- Feature Req: Making the channel argument optional
- Feature Req: Separable Convolution
- Is there any particular reason for puting Blurpool before the skip-connection layers? HOT 3
- Why do deeper CNNs have better shift consistency? HOT 2
- If stride=1, is there a difference between BlurPool and maxpool
- HTTP Error 403: Forbidden when loading weights HOT 9
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