Most publicly available datasets for image classification are with single labels, while images
are inherently multilabeled in our daily life. Such an annotation gap makes many pretrained …

目的人脸属性识别是计算机视觉和情感感知等领域一个重要的研究课题. 随着深度学习的不断
发展, 人脸属性识别取得了巨大的进步. 目前基于深度学习的人脸属性识别方法大多依赖于包含 …

The advance of scene understanding methods based on machine learning relies on the
availability of large ground truth datasets, which are essential for their training and …

While qualitative research can produce a rich understanding of peoples' mind, it requires an
essential and strenuous data annotation process known as coding. Coding can be repetitive …

Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts
where the truth consists of a set of objective winners, knowing a lower and upper bound on …

Image description models are one of the most important and trend topics in the machine-
learning field. Recently, many researches develop systems for image classification and …

Developing modern distributed software systems is difficult in part because they have little
control over the environments in which they execute. For example, hardware and software …

Beyond machine learning's success in the specific tasks, research for learning multiple tasks
simultaneously is referred to as multi-task learning. However, existing multi-task learning …

While topic models can discover patterns of word usage in large corpora, it is difficult to meld
this unsupervised structure with noisy, human-provided labels, especially when the label …

Effectively training machine learning systems with incomplete/partial labels is a practical,
technical problem that solutions described herein attempt to overcome. In particular, an …