Leveraging inexpensive and human intervention-based annotating methodologies, such as
crowdsourcing and web crawling, often leads to datasets with noisy labels. Noisy labels can …

The sample selection approach is very popular in learning with noisy labels. As deep
networks “learn pattern first”, prior methods built on sample selection share a similar training …

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge
(SDX'23). We provide a summary of the challenge setup and introduce the task of robust …

Label-noise learning (LNL) aims to increase the model's generalization given training data
with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different …

In partial-label learning (PLL), each training example has a set of candidate labels, among
which only one is the true label. Most existing PLL studies focus on the instance …

Data-free knowledge distillation aims to learn a small student network from a large pre-
trained teacher network without the aid of original training data. Recent works propose to …

Fatigue is a significant safety concern across various domains, and accurate detection is
vital. However, the commonly employed fine-grained labels (seconds-based) frequently …

The creation of large-scale datasets annotated by humans inevitably introduces noisy
labels, leading to reduced generalization in deep-learning models. Sample selection-based …

Label noise commonly found in real-world datasets has a detrimental impact on a model's
generalization. To effectively detect incorrectly labeled instances previous works have …

Recently, the usage of Contrastive Representation Learning (CRL) as a pre-training
technique improves the performance of learning with noisy labels (LNL) methods. However …