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Found 6 papers, 0 papers with code
Adaptive Transfer Learning: a simple but effective transfer learning
Our empirical evaluations suggest that ATL can help DL models learn more efficiently, especially when available examples are limited.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images.
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data.
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.
Dynamic Input Structure and Network Assembly for Few-Shot Learning
The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception.
