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# System prepended metadata

title: Zero Shot Learning Note

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# Zero Shot Learning Note

## Summary

1. different dataset: with ground truth embedding label 
2. biggest problem: domain swift, hubness problem, semantic gap［2］
3. Solutions for domain swift
    * SAE (visual feature <=reconstruct=> semantic) 
4. Transductive
    * Q: projection domain shift problem [2]
       A1: Semantic Autoencoder for ZSL［3］
           * TODO: add details
           * pros: save all feature value
           * cons: 泛化度不够
    
       A2:生成模型（GAN）
5. DAP(attribute classify acc high but class classify accuracy low) vs  IAP

## Idea
1. extra semantic vector
    1. method
        * mapping visual feature to semanic space which constrained and added by current attribute label (the fisrt n dim will be compared with current attribution label and calculated constructive loss)
        * detached the generator and extend it to every class
        * adding a binary classifier 
    
    2. anlysis
        - Pros:
            * adding extra information which enlarge the intra-class variance
        - Cons:
            * classifier is hard to train


2. GAN based method 
    1. method
        * generate visual sample which conditioned by semantic attribute from specific classes
        * imposing varianiational inference to the visual feature to distentangling the intra-class variance
        * enourage variance of all class following same distribution(both original and generated features)
        * during testing stage, same with paper[4]
        






## Reference
1. GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation
2. Transductive Multi-View ZSL
3. Semantic Autoencoder for Zero-Shot Learning
4. Leveraging the Invariant Side of Generative Zero-Shot Learning