Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …

Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

摘要作为一个十余年来快速发展的崭新领域, 深度学习受到了越来越多研究者的关注,
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学习善于从原始输入数据中 …

鉴于深度学习在学术界和工业界的重要性, 依据数据流向对目前有代表性的深度学习算法进行
归纳和总结, 综述了不同类型深度网络的结构及特点. 首先介绍了深度学习的概念; …

Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …

Abstract Knowledge distillation (KD) achieves promising results on the challenging problem
of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

Semantic segmentation is a key technology for autonomous vehicles to understand the
surrounding scenes. The appealing performances of contemporary models usually come at …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …