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Found 2 papers, 2 papers with code
Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning
We describe federated reconnaissance, a class of learning problems in which distributed clients learn new concepts independently and communicate that knowledge efficiently.
Meta-Learning Initializations for Image Segmentation
We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks.
Ranked #3 on
Few-Shot Semantic Segmentation
on FSS-1000
