Automatic label checking: The missing step in making medical AI reliable

Researchers at Osaka Metropolitan University have discovered a practical way to detect and fix common labeling errors in ...

TillerPET: An AI model for high-throughput phenotyping of rice tiller traits

In a new study published in The Crop Journal on November 7, researchers developed an AI model named TillerPET that enables ...
IEEE

Enhancing Label Noise Robustness for Hyperspectral Image Classification by Neighborhood Contrastive Learning

Abstract: Recent advancements in hyperspectral image classification (HIC) rely on high-quality annotations and thus inevitably suffer from noisy labels. To address the negative effects of noisy labels ...
IEEE

Image Classification Model Based on Contrastive Learning With Dynamic Adaptive Loss

Abstract: As one of the core tasks in vision recognition, image classification is widely used in various scenarios. Most existing mainstream image classification models use the Convolutional Neural ...