In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an
important role in building statistically consistent classifiers. Traditionally, the transition from …

Recent studies on learning with noisy labels have shown remarkable performance by
exploiting a small clean dataset. In particular, model agnostic meta-learning-based label …

Sample selection is a fundamental technique utilized in image classification with noisy
labels. A plethora of sample selection approaches published in the literature are based on a …

Leveraging few-shot datasets in prompt learning for Vision-Language Models eliminates the
need for manual prompt engineering while highlighting the necessity of accurate …

Current deep learning paradigms largely benefit from the tremendous amount of annotated
data. However, the quality of the annotations often varies among labelers. Multi-observer …

Distilling knowledge from a large teacher model to a lightweight one is a widely successful
approach for generating compact, powerful models in the semi-supervised learning setting …

In the evolution of agriculture to its next stage, Agriculture 5.0, artificial intelligence will play a
central role. Controlled-environment agriculture, or CEA, is a special form of urban and …

Training data in federated learning (FL) frameworks can have label noise, since they must
be stored and annotated on clients' devices. If trained over such corrupted data, the models …

With the escalating complexity and investment cost of training deep neural networks
safeguarding them from unauthorized usage and intellectual property theft has become …

Due to the excellent computing efficiency, learning to hash has acquired broad popularity for
Big Data retrieval. Although supervised hashing methods have achieved promising …