  In zero-shot learning (ZSL), conditional generators have been widely used to
generate additional training features. These features can then be used to train
the classifiers for testing data. However, some testing data are considered
"hard" as they lie close to the decision boundaries and are prone to
misclassification, leading to performance degradation for ZSL. In this paper,
we propose to learn clusterable features for ZSL problems. Using a Conditional
Variational Autoencoder (CVAE) as the feature generator, we project the
original features to a new feature space supervised by an auxiliary
classification loss. To further increase clusterability, we fine-tune the
features using Gaussian similarity loss. The clusterable visual features are
not only more suitable for CVAE reconstruction but are also more separable
which improves classification accuracy. Moreover, we introduce Gaussian noise
to enlarge the intra-class variance of the generated features, which helps to
improve the classifier's robustness. Our experiments on SUN,CUB, and AWA2
datasets show consistent improvement over previous state-of-the-art ZSL results
by a large margin. In addition to its effectiveness on zero-shot
classification, experiments show that our method to increase feature
clusterability benefits few-shot learning algorithms as well.
