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Bratolli et al (2020), Rethinking Zero-shot Video Classification: End-to…

- - - - Training classes (labels) must not overlap with test classes (labels). This ensures that a model can
        generalize to scenarios it has not encountered.
      - Domain of pre-training and training set must be different than test set. This is to ensure that domain shifts between training and test set are accommodated in the learning problem
      - ZSL model should perform well on multiple test datasets it has not encountered
  - - - Remove classes of training set on test set
      - This ensures that the information used for learning won't be available for testing.
      - This should be able to test generalization capabilities of model
    - - Pre-trained models on large datasets
        
        Used available 3D convolution models with weights frozen.
      - End-to-end model trained for zero-shot learning
        
        Uses similar architectures as those in the pre-trained models
        
        Difference is that these models were trained for ZSL
  - - - ZSL in this paper outperformed pre-trained networks in using this evaluation protocol
    - - ZSL also outperformed pre-trained networks in using this evaluation protocol.
        
        Results may show some bias since pre-trained networks were not made for this definition of ZSL learning
        
        Results highlighted in this paper may not be that impressive since two networks of different purpose are compared on a problem that fits only one network.