dimensionality-driven-learning
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xingjunm
different result on training
Dear Xingjun: I have tried your method to train the 12-layer-cnn on CIFAR-10 with 20% noise rate,I also observe the decrease and increase of LID score. but in my experiment. the test accuracy is 88.34% by just using cross-entropy as loss function. (in your paper is 73.12%) for 40% noise rate the test accuracy is 84.88% (65.07% in your paper),the results is even better than the results using your D2L method. the only difference during the training process may lies on the preprocessing of the training image (what I use is described in the loss correction method CVPR'17). I wonder why the difference is so much and whether you can try the preprocessing method as in CVPR'17 and train the network again. Thanks !
Hi Fanhan, Will have a look when I have time as I am currently busy with travelling. But from your results it seems CE is even better than any other models which is not reasonable to me. It would be helpful if you can provide some details such as the definition of CE, what is the preprocessing, so that I can replicate your results to understand it precisely.
On Thu, 21 Jun 2018 at 17:22, jiangfanhan [email protected] wrote:
Dear Xingjun: I have tried your method to train the 12-layer-cnn on CIFAR-10 with 20% noise rate,I also observe the decrease and increase of LID score. but in my experiment. the test accuracy is 88.34% by just using cross-entropy as loss function. (in your paper is 73.12%) for 40% noise rate the test accuracy is 84.88% (65.07% in your paper),the results is even better than the results using your D2L method. the only difference during the training process may lies on the preprocessing of the training image (what I use is described in the loss correction method CVPR'17). I wonder why the difference is so much and whether you can try the preprocessing method as in CVPR'17 and train the network again. Thanks !
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Hi Daniel, Thank you for your reply. I have test three method in your paper, they are cross-entropy, forward estimation and D2L on CIFAR-10 dataset. with 20% noise, the test accuracy is 88.34% ,89.35% and 88.65% . with 40% noise, the test accuracy is 84.88%, 86.55% and 85.97% respectively. From other research result the DNN is robust to mild noise so the result just using CE loss is slightly degraded in such case, using correction method can not improve quite much, but the improvement can be observed. The result I get seemed to be closer to the result reported in the CVPR'17 paper, and my preprocessing method is the same as this paper. The CE is defined in the standard way for classification task, I do not take any change.
Regards, Jiangfan
I have the same issue... I can't reproduce the results on the paper using d2l method even though I didn't change the codes... @xingjunm Could you check out the source code?
Hi pokaxpoka, thank you for your report. I do find reproductivity issue when tested with a fresh run on new devices. I will try to fix it. Can you provide the details of your results? Did it fail to converge with CIFAR-10 and 40%/60% noise rates?
Thanks @xingjunm When I tried your method it fails to converge with CIFAR-10 (40%/60% noise rate) and CIFAR-100. Thanks for your update.
I have uploaded an old version of the code old_version/d2l_old.zip, can someone test if this version works?
Thank you all for your interest. And sorry that it took me so long to have time fix the issue. Sorry for the waiting. Good luck to you all with your papers.
@jiangfanhan , I did also observe that the accuracy of CCE is much higher than reported in the paper, if early stopping criterion is used with respect to (noisy) validation data. I got CCE accuracy=0.81
