Deep neural networks typically require a large number of accurately labeled images for
training with cross-entropy loss, and often overfit noisy labels. Contrastive learning has …

Hyperspectral image (HSI) classification techniques have been intensively studied and a
variety of models have been developed. However, these HSI classification models are …

Partial label learning (PLL) is a form of weakly supervised learning, where each training
example is linked to a set of candidate labels, among which only one label is correct. Most …

Abstract Mainstream 2D-3D multi-modal contrastive learning methods perform similarity
clustering on extracted features of different modality data, such as color and spatial …

Label enhancement (LE) refers to the process of recovering label distributions from logical
labels for less ambiguity. Current LE techniques concentrate on learning each instance …

Label Distribution Learning (LDL) is an emerging machine learning paradigm that uses
label distributions instead of logical labels to effectively reduce information loss during the …