Fig. 6
From: Addressing label noise for electronic health records: insights from computer vision for tabular data
Feature similarity distributions at the end of training, for samples correctly and incorrectly labelled, across both similar and different classes without and with NCR. Feature similarity is calculated using cosine similarity as described in Eq. 3. Results shown are for models trained on data with 40% noisy labels