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Found 8 papers, 0 papers with code
Trustworthy Representation Learning Across Domains
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation
In this paper, we propose a pairwise adversarial training approach to augment training data for unsupervised class-imbalanced domain adaptation.
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
In this paper, we propose a challenging and untouched problem: \textit{Open-Set Single Domain Generalization} (OS-SDG), where target domains include unseen categories out of source label space.
Automated Graph Learning via Population Based Self-Tuning GCN
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.
Transferable Feature Learning on Graphs Across Visual Domains
In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains.
Co-embedding of Nodes and Edges with Graph Neural Networks
CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space.
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously.
Glasses Detection Using Convolutional Neural Networks
Glasses detection plays an important role in face recognition and soft biometrices for person identification.
