Comments (4)
Hi, the comparison is in Table 8 of our paper.
Mask type | Region | Random |
---|---|---|
Deit-T | 72.2 | 72.7 |
ResNet50 | 79.3 | 78.3 |
Note that the region mask is used by CutMix.
from tokenmix.
I see. Sorry for missing the table when reading your paper.
Mask type | Region | Random | Block |
---|---|---|---|
Deit-T | 72.2 | 72.7 | 72.7 |
DeiT-S | 79.8 | 80.6 | 80.6 |
ResNet-50 | 79.3 | 78.3 | 79.7 |
From the full table, it seems that random and block sampling work similarly well for ViTs. Block sampling is only advantageous on CNNs?
Also, when apply to CNNs, is the method more like just BlockMix because there is no notion of tokens?
from tokenmix.
Block sampling is only advantageous on CNNs?
Yes.
BlockMix for CNNs
This one should be more accurate.
from tokenmix.
Thank you so much for the classifications. An awesome work!
from tokenmix.
Related Issues (13)
- Code availability?! HOT 3
- How to partition the mask area into multiple separated parts? HOT 1
- 代码在哪里呢??? HOT 1
- No code, no hype, no star, no fork HOT 1
- Have you considered TokenMix in hidden layers? HOT 4
- Not found slurm.conf
- how to add this tokenmix in swin transformer
- Dimension inconsistency of the token labels
- neural activation map part,
- Checkpoints
- TokenMix on CIFAR10
- Sharing the pre-trained transformer trained with TokenMix
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