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Found 9 papers, 7 papers with code
Adversarial Reweighting with $α$-Power Maximization for Domain Adaptation
To theoretically analyze our method, we deduce an upper bound of target domain expected error for PDA, which is approximately minimized in our approach.
Optimal Transport-Guided Conditional Score-Based Diffusion Models
Conditional score-based diffusion model (SBDM) is for conditional generation of target data with paired data as condition, and has achieved great success in image translation.
Keypoint-Guided Optimal Transport
In this paper, we propose a novel KeyPoint-Guided model by ReLation preservation (KPG-RL) that searches for the optimal matching (i. e., transport plan) guided by the keypoints in OT.
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features.
Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning
SSDP aims to reduce domain gap by projecting data to the source domain, while MLCL is a learning scheme to learn discriminative and generalizable features on the projected data.
Adversarial Reweighting for Partial Domain Adaptation
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on
Partial Domain Adaptation
on DomainNet
Domain-Free Adversarial Splitting for Domain Generalization
In this framework, we model the domain generalization as a learning problem that enforces the learner to be able to generalize well for any train/val subsets splitting of the training dataset.
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss
In this paper, we propose a novel adversarial DA approach completely defined in spherical feature space, in which we define spherical classifier for label prediction and spherical domain discriminator for discriminating domain labels.
Should All Temporal Difference Learning Use Emphasis?
ETD was proposed mainly to address convergence issues of conventional Temporal Difference (TD) learning under off-policy training but it is different from conventional TD learning even under on-policy training.
