Comments (2)
Hi, @yaxingwang,
Thanks for you interest.
The short answer is that the student-teacher combinations in this paper are randomly picked. I did not tune student-teacher combination such that our method could perform the best.
Your question actually reveals my initial thoughts of extending previous benchmark. I think most previous methods do not evaluate the performance on variance teacher-student combinations. For instance, Attention Transfer (AT) mainly consider WideResNet, and ResNet, and recent paper Similarity Preserving (SP) mainly consider WideResNet, MobileNet, and ShuffleNet. Additionally, most methods consider the teacher and student models to be of the same architectural type.
So I was trying to test those state-of-the-art methods with randomly picked various student-teacher combinations, and wanted to see how the performance would change. And I also divided these combinations into two groups: (1) of the same architectural type, or (2) of different types. As you may see from the two tables from the paper, some method significantly drops when dealing with group (2).
Lastly, though our CRD is the best among these randomly picked combinations in our paper, I think it's with high probability that it could not consistently be the best in all exhaustive combinations. But in general, it should not be very sensitive to different models, as you can see that we do not require specific architectural inductive bias in our algorithm.
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Thank you for your reply, @HobbitLong . It is good insight to compare the picked randomly student teacher pair. CRD mine additionally the structural knowledge, which is great complementary for KD.
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Related Issues (20)
- Cross modal KD implementation release? HOT 1
- Question on memory consumption for CRD loss when the dataset is very large HOT 3
- Why "opt.nce_k" in dataset cifar100 is 16384? How can I get this ? HOT 2
- test HOT 2
- Error while running the code
- Training scheme for linear probe on STL10 and TinyImagenet
- Problem of the order of the normalization in Similarity-Preserving loss.
- resnet structure seems to be a bit wrong HOT 3
- Failed to download the teacher models HOT 2
- about using the resnet models for cifar10 HOT 1
- crd used in image enhancement task like Denoise\SR\Deblur.
- Ensemble Task Implementation HOT 2
- Question about normalization constant Z_v1 and Z_v2 in the ContrastMemory
- Why using log_softmax instead of softmax? HOT 1
- Hyperparameter Settings for KD on Imagenet
- Is Ensemble distillation also included?
- the result is different in resnet56 HOT 1
- ERROR :run ./fetch_pretrained_teachers.sh HOT 1
- How to use myself datasets? HOT 1
- No dev set split
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