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• W3204231799 abstract "Zero-Shot Learning (ZSL) targets at recognizing unseen categories byleveraging auxiliary information, such as attribute embedding. Despite theencouraging results achieved, prior ZSL approaches focus on improving thediscriminant power of seen-class features, yet have largely overlooked thegeometric structure of the samples and the prototypes. The subsequentattribute-based generative adversarial network (GAN), as a result, alsoneglects the topological information in sample generation and further yieldsinferior performances in classifying the visual features of unseen classes. Inthis paper, we introduce a novel structure-aware feature generation scheme,termed as SA-GAN, to explicitly account for the topological structure inlearning both the latent space and the generative networks. Specifically, weintroduce a constraint loss to preserve the initial geometric structure whenlearning a discriminative latent space, and carry out our GAN training withadditional supervising signals from a structure-aware discriminator and areconstruction module. The former supervision distinguishes fake and realsamples based on their affinity to class prototypes, while the latter aims toreconstruct the original feature space from the generated latent space. Thistopology-preserving mechanism enables our method to significantly enhance thegeneralization capability on unseen-classes and consequently improve theclassification performance. Experiments on four benchmarks demonstrate that theproposed approach consistently outperforms the state of the art. Our code canbe found in the supplementary material and will also be made publiclyavailable." @default.
• W3204231799 created "2021-10-11" @default.
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• W3204231799 date "2021-08-16" @default.
• W3204231799 modified "2023-09-24" @default.
• W3204231799 title "Structure-Aware Feature Generation for Zero-Shot Learning" @default.
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