Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
This work addresses weakly supervised learning for fine-grained classification where labeling is expensive, but it is incremental as it builds on existing NPLL methods.
The paper tackled noisy partial label learning (NPLL) by proposing a framework that assigns pseudo-labels using a weighted nearest neighbor algorithm and trains a classifier with label smoothing, achieving state-of-the-art results on seven datasets with substantial gains in fine-grained benchmarks.
We motivate weakly supervised learning as an effective learning paradigm for problems where curating perfectly annotated datasets is expensive and may require domain expertise such as fine-grained classification. We focus on Partial Label Learning (PLL), a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels (partial label), one of which is the true label. Noisy PLL (NPLL) relaxes this constraint by allowing some partial labels to not contain the true label, enhancing the practicality of the problem. Our work centres on NPLL and presents a framework that initially assigns pseudo-labels to images by exploiting the noisy partial labels through a weighted nearest neighbour algorithm. These pseudo-label and image pairs are then used to train a deep neural network classifier with label smoothing. The classifier's features and predictions are subsequently employed to refine and enhance the accuracy of pseudo-labels. We perform thorough experiments on seven datasets and compare against nine NPLL and PLL methods. We achieve state-of-the-art results in all studied settings from the prior literature, obtaining substantial gains in the simulated fine-grained benchmarks. Further, we show the promising generalisation capability of our framework in realistic, fine-grained, crowd-sourced datasets.