Comments (2)
In many research papers, bilinear CNN (or bilinear pooling) is often implemented on VGG-16. One reason is that ResNet has too many channels, such that the bilinear result is extremely high dimensional (e.g., 2048*2048). Anyway, modify the code in this repository to change the backbone to ResNet is not hard. You may try it as your hands-on experience.
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In many research papers, bilinear CNN (or bilinear pooling) is often implemented on VGG-16. One reason is that ResNet has too many channels, such that the bilinear result is extremely high dimensional (e.g., 2048*2048). Anyway, modify the code in this repository to change the backbone to ResNet is not hard. You may try it as your hands-on experience.
Have u ever tried to do the experiments on resnet? I cannot make it converge.
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Related Issues (20)
- Cannot reproduce accuracy 84% (after step2) HOT 8
- confusion about the parameters of Normalize HOT 1
- out of memory HOT 3
- question about the bilinear pooling operation HOT 1
- "model" in `bilinear_cnn_all.py` HOT 2
- zombie process when using multiple gpu HOT 1
- download the model.pth HOT 1
- 请问,这个PCA要怎么加上去呢?
- About step1 and step2 HOT 2
- 关于双线性汇合特征的规范化
- How to get the mean and variance of the data normalize transform in your code?
- Why there is one feature extractor in BCNN class? HOT 3
- 外积问题
- About memory HOT 1
- Signed square root HOT 4
- In the Step 1, It gives around 56% test set accuracy. I have tried it two times. HOT 2
- About step 2 HOT 2
- Hi HOT 1
- A question about the result of this model HOT 4
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