Deep Label Distribution Learning With Label Ambiguity
نویسندگان
چکیده
منابع مشابه
Incomplete Label Distribution Learning
Label distribution learning (LDL) assumes labels can be associated to an instance to some degree, thus it can learn the relevance of a label to a particular instance. Although LDL has got successful practical applications, one problem with existing LDL methods is that they are designed for data with complete supervised information, while in reality, annotation information may be incomplete, bec...
متن کاملLabel Distribution Learning Forests
Label distribution learning (LDL) is a general learning framework, which assigns a distribution over a set of labels to an instance rather than a single label or multiple labels. Current LDL methods have either restricted assumptions on the expression form of the label distribution or limitations in representation learning. This paper presents label distribution learning forests (LDLFs) a novel...
متن کاملLabel distribution based facial attractiveness computation by deep residual learning
Two challenges lie in the facial attractiveness computation research: the lack of true attractiveness labels (scores), and the lack of an accurate face representation. In order to address the first challenge, this paper recasts facial attractiveness computation as a label distribution learning (LDL) problem rather than a traditional single-label supervised learning task. In this way, the negati...
متن کاملDeep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2 possible label sets when the label dimension L is very large, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by building and modeling an explicit labe...
متن کاملMulti-Label Learning with Weak Label
Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the “full” label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a “partial” set of labels is available. In such cases, the appeara...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2017
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2017.2689998