Feature Generating Networks for Zero-Shot Learning

Yongqin Xian, Tobias Lorenz, Bernt Schiele and Zeynep Akata in IEEE Conference on Computer Vision and Pattern Recognition, 2018

Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network(GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets - CUB, FLO, SUN, AWA and ImageNet - in both the zero-shot learning and generalized zero-shot learning settings.

[Paper]  [Supplement]  [arXiv]  [Code]  [CVPR Daily]

Citation

@inproceedings{xian2018feature,
    author = {Xian, Yongqin and Lorenz, Tobias and Schiele, Bernt and Akata, Zeynep},
    title = {Feature Generating Networks for Zero-Shot Learning},
    booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2018}
}