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This is the official repo for our CVPR22 paper: Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels.
Hello author,
I trained my own data using the method in your paper, and I output all the noise data. I analyzed these noise data, but some of them were not very accurate. Although I achieved high accuracy through the method in the paper, it is based on removing too much data. I want to relax the standards for identifying noisy data, although it may reduce accuracy. What should I do? thanks
我仔细研究了您的代码,发现当cutmix设置为0时,spr函数处理结果返回的clean_set并没有应用,在这种情况下,您的SPR-LNL算是没有实际应用到训练过程中吗?这个cutmix是必须设置为1的吗?为什么要这样呢?也有可能是我对代码的理解有误,希望能够得到您的解答,谢谢!
I'm trying to use the SPR+SR (without CutMix) on my dataset, where noise rate is 30% and 50%. I noticed that after few epochs I see get this error
Traceback (most recent call last):
File "/root/miniconda3/envs/key2med/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/root/SPR-LNL/models/spr.py", line 12, in linear
H = np.dot(np.dot(X, np.linalg.inv(np.dot(X.T, X))), X.T)
File "/root/miniconda3/envs/key2med/lib/python3.10/site-packages/numpy/linalg/linalg.py", line 561, in inv
ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj)
File "/root/miniconda3/envs/key2med/lib/python3.10/site-packages/numpy/linalg/linalg.py", line 112, in _raise_linalgerror_singular
raise LinAlgError("Singular matrix")
numpy.linalg.LinAlgError: Singular matrix
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/root/SPR-LNL/chex_main.py", line 216, in <module>
clean_set = spr(args, ep_stats, clean_set)
File "/root/SPR-LNL/models/spr.py", line 151, in spr
sub_set = res.get()
File "/root/miniconda3/envs/key2med/lib/python3.10/multiprocessing/pool.py", line 774, in get
raise self._value
numpy.linalg.LinAlgError: Singular matrix
My PCA dimensions are 10. Is it because the model asserts all labels as noisy?
The dataset I'm working on is internal with around 200k images and a binary classification task. And your approach is a potential baseline in our paper, I hope you get back soon :)
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