Feature Generating Networks for Zero-Shot Learning

Xian, Yongqin and Lorenz, Tobias and Schiele, Bernt and Akata, Zeynep
(2018) Feature Generating Networks for Zero-Shot Learning.
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 19-21 June 2018, Salt Lake City, USA.
Conference: CVPR IEEE Conference on Computer Vision and Pattern Recognition

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Official URL: https://openaccess.thecvf.com/content_cvpr_2018/ht...


Suffering from the extreme training data imbalance be-tween 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 ofthe art on five challenging datasets – CUB, FLO, SUN, AWA and ImageNet – in both the zero-shot learning and generalized zero-shot learning settings.


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