Joint Distribution Alignment for Universal Domain Adaptation

arXiv:2608.24429 · cs.LG, cs.CV · Submitted 2026-08-25 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Adaptation — 79× in this paper · explained in 1 episode
• Domain Adaptation — 63× in this paper · explained in 5 episodes
• Generalization — 20× in this paper · explained in 13 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Clustering — 7× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Pseudo-labeling — 4× in this paper · explained in 3 episodes
• Pseudo-labels — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Distribution Matching — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Principal Component Analysis — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Steepest Descent — 2× in this paper
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibrated Multiple Uncertainty — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Consensus Clustering — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Separate To Adaptive — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes
• Universal Adaptation Network — 1× in this paper

Sources

• Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
• Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Series
• VisDA: The Visual Domain Adaptation Challenge

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks