Search Results for author:
Found 8 papers, 4 papers with code
XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
Meta-learning, which pursues an effective initialization model, has emerged as a promising approach to handling unseen tasks.
POEM: Polarization of Embeddings for Domain-Invariant Representations
Handling out-of-distribution samples is a long-lasting challenge for deep visual models.
Ranked #18 on
Domain Generalization
on TerraIncognita
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.
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.
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.
