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    CIZSL++: Creativity Inspired Generative Zero-Shot Learning

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    Type
    Preprint
    Authors
    Elhoseiny, Mohamed cc
    Yi, Kai cc
    Elfeki, Mohamed
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    King Abdullah University Of Science And Technology , KAUST, Thuwal, Saudi Arabia.
    Date
    2021-01-01
    Permanent link to this record
    http://hdl.handle.net/10754/666828
    
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    Abstract
    Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of ZSL, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art. First, we propose CIZSL-v1 as a creativity inspired model for generative ZSL. We relate ZSL to human creativity by observing that ZSL is about recognizing the unseen, and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. Second, CIZSL-v2 is proposed as an improved version of CIZSL-v1 for generative zero-shot learning. CIZSL-v2 consists of an investigation of additional inductive losses for unseen classes along with a semantic guided discriminator. Empirically, we show consistently that CIZSL losses can improve generative ZSL models on the challenging task of generalized ZSL from a noisy text on CUB and NABirds datasets. We also show the advantage of our approach to Attribute-based ZSL on AwA2, aPY, and SUN datasets. We also show that CIZSL-v2 has improved performance compared to CIZSL-v1.
    Citation
    https://openaccess.thecvf.com/content_ICCV_2019/papers/Elhoseiny_Creativity_Inspired_Zero-Shot_Learning_ICCV_2019_paper.pdf
    Publisher
    arXiv
    arXiv
    2101.00173
    Additional Links
    https://arxiv.org/pdf/2101.00173
    Collections
    Preprints; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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