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Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization
However, this is likely to restrict the potential of the target model and some transferred knowledge from the source can interfere with the training procedure.
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators
Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain.
Discriminative Few-Shot Learning Based on Directional Statistics
Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy.
Auto-Meta: Automated Gradient Based Meta Learner Search
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning.
Meta Continual Learning
This ability is limited in the current deep neural networks by a problem called catastrophic forgetting, where training on new tasks tends to severely degrade performance on previous tasks.
