We pro- posed a propagation regularizer which led to efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias.
We propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias.
We propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias.
In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing ...
A propagation regularizer to suppress confirmation bias and allow semi-supervised learning to proceed stably with just 1-2 labeled samples per class. 2. A model ...
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples ... The proposed methods show 70. 9%, 30. 3%, and 78. 9% accuracy on ...
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples. [pdf]. Noo-ri Kim, Jee-Hyong Lee. CVPR 2022. Towards Discovering ...
Oct 21, 2024 · It's about taking advantage of those unlabeled samples by leveraging the small amount of labeled data to guide the learning process.
Missing: Extremely | Show results with:Extremely
Oct 28, 2024 · This paper proposes a simple yet effective framework, active self-semi-supervised learning (AS3L). AS3L bootstraps semi-supervised models with prior pseudo- ...
Propagation Regularizer for Semi-supervised Learning with Extremely Scarce Labeled Samples · Noo-Ri KimJeehyun Lee. Computer Science, Mathematics. Computer ...