Reviews: Domain-Invariant Projection Learning for Zero-Shot Recognition

The authors present an algorithm for zero-shot learning based on learning a linear projection between the features of a pre-trained convnet and a semantic space in which to do nearest neighbors classification. Overall, I found the exposition of the paper very confusing, and I am still struggling to understand the exact set-up in the experiments and theorems after several re-readings. I'm not 100% certain from the paper what the authors are actually doing and what they have available as training data. In particular, the authors denote the test images by D_u, which they are sure to point out are unlabeled. They then define l_i {(u)} to be the label of test point x_i {(u)} - how can the label be included in a set of test images that is unlabeled?