Towards Few-Shot Learning in the Open World: A Review and Beyond
Human intelligence is characterized by our ability to absorb and apply knowledge from the
world around us, especially in rapidly acquiring new concepts from minimal examples …
world around us, especially in rapidly acquiring new concepts from minimal examples …
[HTML][HTML] Exploiting unlabeled data in few-shot learning with manifold similarity and label cleaning
Few-shot learning investigates how to solve novel tasks given limited labeled data.
Exploiting unlabeled data along with the limited labeled has shown substantial improvement …
Exploiting unlabeled data along with the limited labeled has shown substantial improvement …
UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
Transductive few-shot learning has recently triggered wide attention in computer vision. Yet,
current methods introduce key hyper-parameters, which control the prediction statistics of …
current methods introduce key hyper-parameters, which control the prediction statistics of …
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Few-shot learning (FSL) aims to develop a learning model with the ability to generalize to
new classes using a few support samples. For transductive FSL tasks, prototype learning …
new classes using a few support samples. For transductive FSL tasks, prototype learning …
Few-shot image classification based on gradual machine learning
N Chen, X Kuang, F Liu, K Wang, L Zhang… - Expert Systems with …, 2024 - Elsevier
Few-shot image classification aims to accurately classify unlabeled images using only a few
labeled samples. The state-of-the-art solutions are built by deep learning, which focuses on …
labeled samples. The state-of-the-art solutions are built by deep learning, which focuses on …