Temporal Ensembling-based Deep k-Nearest Neighbours for Learning with Noisy Labels
Alexandra-Ioana Albu · 2023
Label noise can significantly affect the generalization of deep neural networks.Nevertheless, it is omnipresent in real world applications.This paper introduces an approach for identifying the samples from a dataset which are likely to have correct annotations.The proposed method computes the agreement of a sample with its nearest neighbours retrieved from the feature space provided by a neural network.We introduce a temporal ensembling strategy which takes into account the agreement scores obtained by a sample during previous training epochs.The superiority of our approach over several baselines is shown on image classification datasets.