Papathanail, IoannisIoannisPapathanailLu, YaYaLuGhosh, ArindamArindamGhoshMougiakakou, StavroulaStavroulaMougiakakouDel Bimbo, AlbertoCucchiara, RitaSclaroff, StanFarinella, Giovanni MariaMei, TaoBertini, MarcoEscalante, Hugo JairVezzani, Roberto2024-10-052024-10-052021-02-21https://boris-portal.unibe.ch/handle/20.500.12422/56307The objective of multi-label image classification is to recognise several objects that appear within a single image. In the current paper, we consider the task of multi-label food recognition, where the images contain foods for which the labels in the training set are noisy, as they are annotated by inexperienced annotators. We now propose that a noise adaptation layer should be appended to a pretrained baseline model, in order to make it possible to learn from these noisy labels. From the baseline model, predictions are made on the training set and a confusion matrix is created from these predictions and the noisy labels. This confusion matrix is used to initialise the weights of the noise layer and the full model is retrained on the training set. The final predictions for the testing set are made from the baseline model, after its weights have been readjusted by the noise layer. We show that the final model significantly improves performance on noisy datasets.en600 - Technology600 - Technology::610 - Medicine & health600 - Technology::620 - Engineeringconference_item10.7892/boris.15285510.1007/978-3-030-68821-9_49