In the standard label smoothing regime, label smoothing is applied to every training example. I have a real world dataset where there is some label noise between 2 of the 30 classes I’m training the classifier on. Basically, two of the classes may be mislabeled as one another. I was hoping to try to use label smoothing as a way to compensate for some of the noise in these labels.

Does anyone have an idea of how to implement this efficiently?

You have a multi-class (30 classes) classification problem. You know
that for most of your classes, your ground-truth target labels are
correct, but your labels sometimes mix up two of your classes, say 4
and 9, Let’s say that a sample labelled 4 is actually a 9 25% of the
time, and that a sample labelled 9 is actually a 4 10% of the time.

You will (most likely) want to use cross-entropy loss, but pytorch only
provides a version that takes integer categorical class labels for its target. In your case, you want what I call soft labels, and will have
to write your own soft-label version of cross-entropy. See this post
for an implementation:

Now let me assume that your (sometimes incorrect) target labels
are given as integer categorical labels. First use one_hot() (followed
by float()) to convert your categorical labels into soft labels (that all
happen to be zero or one). Then whenever a sample is labelled 4
(target[i, 4] == 1.0), set target[i, 4] = 0.75 and target[i, 9] = 0.25. Similarly, when target[i, 9] == 1.0, set target[i, 9] = 0.90, and target[i, 4] = 0.10.

You then feed the soft-label target you constructed into the soft-label
cross-entropy you implemented yourself.