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Found 7 papers, 5 papers with code
Subspace Adaptation Prior for Few-Shot Learning
Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent.
Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques
Whilst meta-learning techniques have been observed to be successful at this in various scenarios, recent results suggest that when evaluated on tasks from a different data distribution than the one used for training, a baseline that simply finetunes a pre-trained network may be more effective than more complicated meta-learning techniques such as MAML, which is one of the most popular meta-learning techniques.
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
Stateless Neural Meta-Learning using Second-Order Gradients
Deep learning typically requires large data sets and much compute power for each new problem that is learned.
A Survey of Deep Meta-Learning
Meta-learning is one approach to address this issue, by enabling the network to learn how to learn.
