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Showing 1 to 2 of 2 for “"cross-domain few-shot learning"”.

  1. Feature extractor stacking for cross-domain few-shot learning

    Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that …

    waikato-masters Repository record for Feature extractor stacking for cross-domain few-shot learning (opens in a new tab)

  2. Learning with Limited Labeled Data: Techniques and Applications

    … development and evaluation of advanced machine learning algorithms to solve the following research questions: (1) How to learn novel classes with limited labeled data, (2) How to adapt a large pre-trained model to the target domain if only unlabeled data is available, (3) How to boost the …

    vt Repository record for Learning with Limited Labeled Data: Techniques and Applications (opens in a new tab)