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Found 9 papers, 2 papers with code
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation
The task-conditioned feature transformation allows an effective utilization of the semantic information in novel classes to generate tight segmentation masks.
Few-Round Learning for Federated Learning
Of central issues that may limit a widespread adoption of FL is the significant communication resources required in the exchange of updated model parameters between the server and individual clients over many communication rounds.
Task-Adaptive Feature Transformer for Few-Shot Segmentation
Few-shot learning allows machines to classify novel classes using only a few labeled samples.
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
The base and novel classifiers quickly adapt to a given task by utilizing the TAR.
Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification
Our method employs explicit task-conditioning in which unlabeled sample clustering for the current task takes place in a new projection space different from the embedding feature space.
CAFENet: Class-Agnostic Few-Shot Edge Detection Network
Since there is no existing dataset for few-shot semantic edge detection, we construct two new datasets, FSE-1000 and SBD-$5^i$, and evaluate the performance of the proposed CAFENet on them.
Semi-Supervised Few-Shot Learning with a Controlled Degree of Task-Adaptive Conditioning
Our method employs explicit task-conditioning in which unlabeled sample clustering for the current task takes place in a new projection space different from the embedding feature space.
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
The training loss is obtained based on a distance metric between the query and the reference vectors in the projection space.
Meta-Learner with Linear Nulling
We propose a meta-learning algorithm utilizing a linear transformer that carries out null-space projection of neural network outputs.
