Comments (2)
Thanks for your interest! I think you can get the estimated noise by doing output minus input, since the noise is additive. Specifically, estimated noise = x - net(x), but make sure the input is normalized.
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Thanks Ruiz, turns out that the normalization was the missing ingredient. Just in case you're interested, the reason I seek the noise is to perhaps apply RemixIT to the model such that I can try unsupervised domain adaptation.
Again, I learned a lot from the paper. So thanks for your work!
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Related Issues (20)
- the change of gen_loss during training HOT 1
- RuntimeError
- RuntimeError HOT 2
- About the decreasing of loss HOT 1
- Can not reproduce the results HOT 12
- Training can get stuck HOT 6
- Inferior results trained from scratch HOT 7
- RuntimeeError HOT 1
- Can not reproduce the results HOT 3
- Training GPU requirements HOT 1
- File "pesq/cypesq.pyx", line 1, in init cypesq ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it)
- File "/anaconda3/envs/cmg/lib/python3.9/site-packages/torch/nn/parallel/distributed.py", line 578, in __init__ dist._verify_model_across_ranks(self.process_group, parameters) RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:957, invalid usage, NCCL version 21.0.3 ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc). HOT 2
- How do you resample to 16000? HOT 2
- 时域Loss计算疑惑
- the training speed confusion
- My server has a 3090, but reports that I don't have a gpu HOT 1
- Test set requirements when training
- epochs HOT 1
- 模型训练的采样率以及显卡训练配置咨询 HOT 1
- Can the model be open sourced?
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