pLSA-based zero-shot learning
Wai Lam Hoo, Chee Seng Chan · 2013
Current zero-shot learning methods relied on attributes to describe the unseen class characteristics, using the learned seen class model. However, these approaches required extensive attribute labels on each object class, and a well-defined, attributes relationship between the seen and unseen class with the aid of human knowledge. In this work, we avoid these with a novel learning process using the probabilistic Latent Semantic Analysis (pLSA). We replace the attributes with topic model and extend the representation as a mapping algorithm to object classes, so that zero-shot learning would be possible. With this, less annotated class information is required to achieve similar performance. Evaluations on three public datasets had shown the effectiveness of our proposed method.