( Image credit: Prototypical Networks for Few shot Learning in PyTorch )
You can view blog posts such as this to get a high-level understanding:
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Continual zero-shot learning(CZSL) is a new domain to classify objects sequentially the model has not seen during training.
Zero-shot learning aims to recognize unseen objects using their semantic representations.
Continued pretraining offers improvements, with an average accuracy of 44. 05%.
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification.
To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework.
However, zero-shot learning models assume that all seen classes should be known beforehand, while incremental learning models cannot recognize unseen classes.
Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU).
Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL.
We show that the key reason is that the generation is not Counterfactual Faithful, and thus we propose a faithful one, whose generation is from the sample-specific counterfactual question: What would the sample look like, if we set its class attribute to a certain class, while keeping its sample attribute unchanged?
The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.