Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …

With the recent renaissance of deep convolutional neural networks (CNNs), encouraging
breakthroughs have been achieved on the supervised recognition tasks, where each class …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

Recent work has managed to learn cross-lingual word embeddings without parallel data by
mapping monolingual embeddings to a shared space through adversarial training …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …

We present a generative framework for generalized zero-shot learning where the training
and test classes are not necessarily disjoint. Built upon a variational autoencoder based …

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is
difficult to obtain for most language pairs. This has motivated an active research line to relax …

Zero-shot learning (ZSL) models rely on learning a joint embedding space where both
textual/semantic description of object classes and visual representation of object images can …