zysxmu / intraq Goto Github PK

Thanks for your work！I notice that in network fine-tuning stage，you used a GAN network to generate data and used the data to train. But this was not mentioned in the paper. If you have time to explain the reason, I'd be very grateful.

Thanks for your work!
You mentioned in the paper that "Both learning rates are decayed by 0.1 every 100 fine-tuning epochs." But I noticed that in the code, you set the LR tuning strategy to [20,40,60] for CIFAR10. Looking forward to some explanation from you on this.
Thank you again.

Hello!
I was wondering why the result of ZeroQ you reported in the Table.1 of your paper (W4A4, 60.68%on ImageNet) is so high? It's even higher than GDFQ. I clone ZeroQ's code and the result is similar to GDFQ's paper(~26%). Besides, since ZeroQ's synthetic data don't have its label(without IL), how do you perform fine-tuning on 4-bit ResNet18(caption of Table.1)?
Looking forward to your reply! Thx!

Hi, thanks for your work!
It seems that there is a generator being trained in trainer_direct.py, while the paper says the data for fine-tuning is obtained by optimizing Gaussian noise but not a generator. I am not familiar with zero-shot quantization, and this makes me confused. Could you please help me figure it out? Thanks!

In my experiments, I can only get 72.70% on 3w3a CIFAR-10, which is far from the result reported in the paper

Hi, thanks for your work!
I was wondering why the result of DSG in the Table.3 of your paper is lower than ZeroQ? Since in their paper they claim that DSG performs better than ZeroQ. As far as I know, there was no official open-source code for DSG, but I noticed in your paper (Section 4.1) that you used open-source code. So, did you reproduce DSG personally or had I misunderstood something? Maybe there's something wrong with DSG?

Thanks for your reply

The question of below is sloved. mkdir hardsample.

Thanks for sharing your code. It was very helpful in understanding your interesting work.
I have some questions about your code.

For generating the data for cifar100, it seems that the marginal distance constraints (loss_cosineDistance and loss_cosineDistance_upper in distill_data.py) are both 0 during the data generation process.
Is this a code error or am I missing something?

Also, could you explain why you used 1-CosineSimilarilty instead of CosineSimilarity in your code?

Thanks in advance!

Thanks for your good work on data-free quantization and the release of the code.

Zero-shot adversarial quantization(ZAQ) is also a data-free quantization method with adversarial exploration. I notice that both IntraQ and AIT are not compared to the ZAQ method. Are they solving different problems or having different experiment settings?

Thanks for your tips.