pxiangwu / plc Goto Github PK

The Code uses "lrt_correction"(LRT, ICLR2020) by default.
And the "prob_correction" seems not match PLC, it considers "cur_prob_distri[y_noise[i]]/top_probs[-1]", and no "f(x_i)-1/2,"t>=m","theta=1/2-T" can be found.

Hi,
thanks for sharing your implementation. I have some questions about it:

1. Does it also work on tabular data?
2. Is the code tailored to the datasets used in the paper or can one apply it to any data?
3. Is it possible to identify the noisy instances (return the noisy IDs or the clean set)?

Thanks!

In the probability correction algorithm, the generated labels should be multinoimally distributed among all the aviable labels. However according to the code given, it will just be 0 or 1

The issue come because the code

will result in a scalar top_probs. Hence the code to generate new labels

Moreover the normalization here

Is this intended behavior?

Hello, pxiangwu. You did a great work in the PLC correction. I am confused when reading the "prob_correction“ function in the utils.py file (line 343-355). You mentioned that " If the predicted confidence exceeds this threshold, we use label correction based on likelihood ratio test. Otherwise, we use probabilistic label correction.“ But the 351 th line "flipped = flipper.multinomial(1, top_probs, 1)[0]" seems to generate a random value. Do the lines 350-355 correspond to the PLC corrrection? By the way, when the prediction has a lower confidence than the confidence threshold, its label must be changed using the codes (350-355) ? I am sorry if I understand wrongly. Thank you for your time.