no code implementations • 29 Apr 2015 • Ubai Sandouk, Ke Chen
We conduct experiments on three public music tag collections -namely, CAL500, MagTag5K and Million Song Dataset- and compare our approach to a number of state-of-the-art semantics learning approaches.
no code implementations • 17 Jun 2015 • Ubai Sandouk, Ke Chen
By means of pattern aggregation and probabilistic topic models, our Siamese architecture captures contextualized semantics from the co-occurring descriptive terms via unsupervised learning, which leads to a concept embedding space of the terms in context.
no code implementations • 1 Jun 2016 • Ubai Sandouk, Ke Chen
Thus, our approach allows both seen and unseen labels during the concept embedding learning to be used in the aforementioned instance mapping, which makes multi-label ZSL more flexible and suitable for real applications.