Comments (2)
It may be caused by the different initializations of the neural network by different versions of Pytorch. If you find it is a little overfitting, you can use a smaller initial learning rate instead of 0.005, e.g., 0.001. Actually, we had tested the released code on the miniImageNet dataset in our own machine and you can find our tested results in the file of DN4/results. By the way, can you tell me the version of Pytorch you used and the results you obtained? I can retest the code on other Pytorch versions and machines.
It is a little trick. We didn't pay a lot of effort on this hyperparameter. In our experiments, we find 0.05 is okay because we use 0.005 as the initial learning rate for the first 10 epoch. Maybe you can use 0.001 as the initial learning rate, and change it by 0.5. We believe we should have a better decay mechanism of the learning rate, but this is not the main concern of our paper. Thank you.
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@IbsenChan Could you share the results you have reproduced with this code? Thank you!
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Related Issues (20)
- a question about ablation study HOT 6
- The results are different from the paper on miniImagenet dataset HOT 28
- About BN parameters HOT 3
- why you used --ravi HOT 3
- Some questions about resnet HOT 6
- what about multiGPU training? HOT 5
- The result of benchmark models on CUB dataset HOT 3
- Testing in a single image
- Does this DN4 network contains a pre-triaing stage? HOT 7
- About preprocessing on Stanford_Cars HOT 2
- Question on paper figure HOT 2
- BN parameters followup question HOT 1
- Inference on one image only HOT 1
- Could you please share your split csv files? HOT 2
- A question about k-nearest neighbors HOT 1
- CUB HOT 3
- 可视化
- batch_size只能设为1? HOT 1
- about ablation study HOT 1
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