The current state-of-the-art in feature learning relies on the supervised
learning of large-scale datasets consisting of target content items and their
respective category labels. However, constructing such large-scale
fully-labeled datasets generally requires painstaking manual effort. One
possible solution to this problem is to employ community contributed text tags
as weak labels, however, the concepts underlying a single text tag strongly
depends on the users. We instead present a new paradigm for learning
discriminative features by making full use of the human curation process on
social networking services (SNSs). During the process of content curation, SNS
users collect content items manually from various sources and group them by
context, all for their own benefit. Due to the nature of this process, we can
assume that (1) content items in the same group share the same semantic concept
and (2) groups sharing the same images might have related semantic concepts.
Through these insights, we can define human curated groups as weak labels from
which our proposed framework can learn discriminative features as a
representation in the space of semantic concepts the users intended when
creating the groups. We show that this feature learning can be formulated as a
problem of link prediction for a bipartite graph whose nodes corresponds to
content items and human curated groups, and propose a novel method for feature
learning based on sparse coding or network fine-tuning.

提出一种基于人类编辑过程的新典范，使得特征学习可以通过人类筛选作为弱标签的文本标签进行学习。通过将人类筛选的内容标记为弱标签，提出一种新的用于学习鉴别特征的框架，其中我们将鉴别特征定义为用于表示用户在创建组时想要表达的语义概念空间的表示。