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Found 7 papers, 6 papers with code
Language Semantic Graph Guided Data-Efficient Learning
Therefore, to achieve data-efficient learning, researchers typically explore approaches that can leverage more related or unlabeled data without necessitating additional manual labeling efforts, such as Semi-Supervised Learning (SSL), Transfer Learning (TL), and Data Augmentation (DA).
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning Paradigm
To address this issue, this paper presents a novel approach that seeks to leverage linguistic knowledge for data-efficient visual learning.
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty.
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
We seek an adaptive balance between the structural integrity and the flexibility for $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$, while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e. g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization.
Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation
To alleviate these issues, we propose to simultaneously conduct feature alignment in two individual spaces focusing on different domains, and create for each space a domain-oriented classifier tailored specifically for that domain.
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
Dynamic Domain Adaptation for Efficient Inference
Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy.
