Comments (3)
Yes, we had observed the same degradation without fixing the BN parameters. This is the reason why we fix the parameters of Batch Normalization after 10000 episodes. We don't know the true reason of this phenomenon yet. We guess BN will affect the generalization performance of models. Actually, the research on BN is very hot recently. Thanks.
from dn4.
Thanks a lot for your insightful analysis. And do you think other metric-based few-shot classification models, i.e., Protonet, can also benefit from this fixed-parameter setting?
from dn4.
You are welcome. Yes, we had reimplemented Protonet based on the same framework, and we found this setting can gain about 1% performance improvement.
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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
- 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
- 代码报错 HOT 1
- 细粒度数据集准确率 HOT 3
- CUB HOT 3
- 可视化
- batch_size只能设为1? HOT 1
- about ablation study HOT 1
- Experiment results HOT 2
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