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Found 13 papers, 8 papers with code
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing
Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios.
DCANet: Dense Context-Aware Network for Semantic Segmentation
As the superiority of context information gradually manifests in advanced semantic segmentation, learning to capture the compact context relationship can help to understand the complex scenes.
Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNets
To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective.
1D Probabilistic Undersampling Pattern Optimization for MR Image Reconstruction
We propose a 1D probabilistic undersampling layer, to obtain the optimal undersampling pattern and its probability distribution in a differentiable way.
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
In particular, our proposed HoMM can perform arbitrary-order moment tensor matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL).
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation
However, since the domain shift between source and target domains, only using the deep features for sample selection is defective.
Ranked #6 on
Partial Domain Adaptation
on Office-31
Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation
Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global distribution alignment without considering class information across domains.
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion
The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.
Truncated nuclear norm regularization for low-rank tensor completion
It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix.
Parameter Transfer Extreme Learning Machine based on Projective Model
Unlike the existing parameter transfer approaches, which incorporate the source model information into the target by regularizing the di erence between the source and target domain parameters, an intuitively appealing projective-model is proposed to bridge the source and target model parameters.
Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities.
Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization
Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing.
