Comments (1)
Hi, deep all simply means that the classifier is trained on the source datasets without performing the Jigsaw task and without ever seeing the target. If you use train_jigsaw and set jig_weight to 0 and bias_whole_image to 1 you should get deep all.
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Related Issues (20)
- About requisite of this repository HOT 1
- About the performance on VLCS dataset HOT 4
- Hi, Could you give some details about the generation of permutations based on Hamming distance? HOT 1
- Questionable implementation of parsing boolean args HOT 1
- The performance in paper about DeepAll HOT 20
- PyTorch version of this code? HOT 1
- Comparison on PACS and VLCS HOT 4
- Python Version HOT 1
- TypeError: alexnet() got an unexpected keyword argument 'jigsaw_classes' HOT 1
- Error with argparser HOT 3
- what is the meanin of patch_based? HOT 1
- Doubt about experimental results HOT 1
- Office home dataset HOT 4
- Why is the input data composed with 9 grid? HOT 2
- python version HOT 2
- evaluate on VLCS dataset HOT 5
- Train on different datasets HOT 2
- The question about the classifier
- understanding bias_whole_image
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