Comments (6)
Hi, thanks for your interest. Have you applied the sparsity constraints (L1-norm) on the scaling factors? And the sparsity constraints should be applied individually on disjoint parts of channels (as shown in Figure 2).
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Thank you very much for the quick response! I’m sure the sparsity constraints (L1-norm) are applied on the scaling factors(as shown in Figure 2). But the result of the scaling factors visualization is shown in the following figure, two colors represent different modalities, which are very close
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In this figure, it seems the sparsity constraints have little effect on the scaling factors, which is abnormal. Have you tried to increase lambda (in Eq. (4))?
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Thank you very much for your great suggestion, The scaling factors visualization became reasonable after I tried to increase lamda. It seems like the sparsity constraints with higher lamda increases the difference of scaling factors between different modalities and highlights the non-negligible channels, which was discussed in detail in the paper [33](Learning Efficient Convolutional Networks through Network Slimming).
I also want to ask a question, have you tried using the ratio of the scaling factors to decide whether to exchange channels? i.e. use ratio instead of threshold, such as exchange channels if the ratio of the scaling factors is higher than 0.2. I did some experiments like this but it didn't work very well, may I ask what is the problem?
Thank you very much!
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Hi, during our early exploration, we also tried to exchange channels by using ratios instead of the threshold. Yet we find the current threshold-guided design is much better than ratio-guided one.
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Thank you very much for your reply!
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Related Issues (17)
- Paper request HOT 2
- Query regarding applicability to other tasks HOT 2
- Formatting iOS Lidar Depth Data For Transfer-Learning HOT 2
- Non colorised masks HOT 1
- How can get the datasets of "train" and "val" HOT 4
- 为什么对rgb, depth和ens的loss求和? HOT 1
- In the end of CE HOT 9
- Sparsity constraint in channel exchanging HOT 5
- 关于多模态的形式 HOT 1
- About visualization figures in paper HOT 7
- About where the mean(ensemble) is calculated HOT 5
- Some question about the image size HOT 2
- 最终表示的问题 HOT 4
- For fusion of 3 modalities HOT 4
- some question for image size HOT 6
- Method to choose a good lambda (in Equation 4) HOT 4
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