There has been significant attention devoted to the effectiveness of various domains, such
as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the …

Deep neural networks have proven to be highly effective when large amounts of data with
clean labels are available. However their performance degrades when training data …

Training a classifier on web-crawled data demands learning algorithms that are robust to
annotation errors and irrelevant examples. This paper builds upon the recent empirical …

Label noises, categorized into closed-set noise and open-set noise, are prevalent in real-
world scenarios and can seriously hinder the generalization ability of models. Identifying …

In classification tasks with noisy labels, eliminating the interference of noisy label samples in
the dataset is the key to improving network performance. However, the distribution between …

Aiming at the problem of insufficient label data in the pipeline leak detection field, this paper
proposes a pseudolabel (PL) adaptive learning method based on multiscale convolutional …

Recent advancements in unsupervised object re-identification have witnessed remarkable
progress, which usually focuses on capturing fine-grained semantic information through …

Robust training with noisy labels is a critical challenge in image classification, offering the
potential to reduce reliance on costly clean-label datasets. Real-world datasets often contain …

In recent years, the remarkable success of deep neural networks (DNNs) in computer vision
is largely due to large-scale, high-quality labeled datasets. Training directly on real-world …

Mitigating the detrimental effects of noisy labels on the training process has become
increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale …