Theoretical Analysis of Label Distribution Learning
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 Learning by Optimal Transport
Label distribution learning (LDL) is a novel learning paradigm to deal with some real-world applications, especially when we care more about the relative importance of different labels in description of an instance. Although some approaches have been proposed to learn the label distribution, they could not explicitly learn and leverage the label correlation, which plays an importance role in LD...
متن کاملSense Beauty by Label Distribution Learning
Beauty is always an attractive topic in the human society, not only artists and psychologists, but also scientists have been searching for an answer – what is beautiful. This paper presents an approach to learning the human sense toward facial beauty. Different from previous study, the human sense is represented by a label distribution, which covers the full range of beauty ratings and indicate...
متن کاملSoft Video Parsing by Label Distribution Learning
In this paper, we tackle the problem of segmenting out a sequence of actions from videos. The videos contain background and actions which are usually composed of ordered sub-actions. We refer the sub-actions and the background as semantic units. Considering the possible overlap between two adjacent semantic units, we utilize label distributions to annotate the various segments in the video. The...
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: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33015256