Learn what meta-learning is, how it improves neural networks' generalization in few-shot learning scenarios, and how to implement it in Python. Agree & Join LinkedIn ...

Be aware that the term “few-shot” learning might be used as an umbrella term to describe any situation where a model is being trained with very little data. Approaches to Few-Shot Learning. Most ...

Few-shot learning is actually an application of meta-learning where we need a model to make very accurate predictions provided that the data fed into the system is significantly less. The few-shot ...

Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage ...

Humans adapt to new environments and information with only a few samples. Future deep-learning models will adapt and learn from only a few samples. MAML and Reptile are gradient based meta-learning ...

Humans adapt to new environments and information with only a few samples. Future deep-learning models will adapt and learn from only a few samples. MAML and Reptile are gradient based meta-learning ...

Keywords: meta-learning, few-shot learning, situational meta-task, ensemble model, image recognition. Citation: Zhang Z, Zhou L, Wu Y and Wang N (2024) The meta-learning method for the ensemble model ...

We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adaptable to many target ...