Papers › Feature Generating Networks for Zero-Shot Learning
Feature Generating Networks for Zero-Shot Learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata
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.
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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Generalized Zero-Shot Learning | SUN Attribute | f-CLSWGAN | Harmonic mean | 39.4 | #7 of 9 | Archive leaderboard | report |
| Zero-Shot Learning | CUB-200-2011 | f-CLSWGAN | average top-1 classification accuracy | 57.3 | #12 of 14 | Archive leaderboard | report |
| Zero-Shot Learning | SUN Attribute | f-CLSWGAN | average top-1 classification accuracy | 60.8 | #8 of 9 | Archive leaderboard | report |
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Methods
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