This is the scenario of fraud detection.
Say model was trained on transactions that were labeled as “clean” and “fraudulent”.
Later investigation determined that number of “clean” transaction were actually fraudulent and labels were corrected.
Is there a proper way to re-train models?
What’s a general approach in Deep Learning to “correct” models that were already pre-trained on mislabeled data without starting from scratch?
Is there are optimized ways for models to “forget” certain data inputs?
Gleb