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    Domain-Aware Continual Zero-Shot Learning

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    DACZSL_MS_Thesisi_Kai_Yi_Final.pdf
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    Description:
    Final MS Thesis
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    Type
    Thesis
    Authors
    Yi, Kai cc
    Advisors
    Elhoseiny, Mohamed cc
    Committee members
    Wonka, Peter cc
    Ghanem, Bernard cc
    Michels, Dominik
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-11-29
    Permanent link to this record
    http://hdl.handle.net/10754/673833
    
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    Abstract
    We introduce Domain Aware Continual Zero-Shot Learning (DACZSL), the task of visually recognizing images of unseen categories in unseen domains sequentially. We created DACZSL on top of the DomainNet dataset by dividing it into a sequence of tasks, where classes are incrementally provided on seen domains during training and evaluation is conducted on unseen domains for both seen and unseen classes. We also proposed a novel Domain-Invariant CZSL Network (DIN), which outperforms state-of-the-art baseline models that we adapted to DACZSL setting. We adopt a structure-based approach to alleviate forgetting knowledge from previous tasks with a small per-task private network in addition to a global shared network. To encourage the private network to capture the domain and task-specific representation, we train our model with a novel adversarial knowledge disentanglement setting to make our global network task-invariant and domain-invariant over all the tasks. Our method also learns a class-wise learnable prompt to obtain better class-level text representation, which is used to represent side information to enable zero-shot prediction of future unseen classes. Our code and benchmarks are made available at https://zero-shot-learning.github.io/daczsl.
    Citation
    Yi, K. (2021). Domain-Aware Continual Zero-Shot Learning. KAUST Research Repository. https://doi.org/10.25781/KAUST-D16H1
    DOI
    10.25781/KAUST-D16H1
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-D16H1
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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