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Methodology
← Learning Types
Machine Learning
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Learning Types
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Domain Adaptation
1154 directly classified papers
Papers per year
2006: 1
2007: 3
2008: 3
2009: 2
2010: 3
2011: 3
2012: 4
2013: 10
2014: 6
2015: 3
2016: 3
2017: 26
2018: 32
2019: 61
2020: 128
2021: 153
2022: 186
2023: 161
2024: 192
2025: 162
2026: 12
Papers
Seeking Similarities Over Differences: Similarity-Based Domain Alignment for Adaptive Object Detection
ICCV 2021
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
ICCV 2021
Meta Learning on a Sequence of Imbalanced Domains With Difficulty Awareness
ICCV 2021
Transporting Causal Mechanisms for Unsupervised Domain Adaptation
ICCV 2021
Unsupervised Domain Adaptive 3D Detection With Multi-Level Consistency
ICCV 2021
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
ICCV 2021
Domain Adaptive Semantic Segmentation With Self-Supervised Depth Estimation
ICCV 2021
Information-Theoretic Regularization for Multi-Source Domain Adaptation
ICCV 2021
Domain adaptation under structural causal models
JMLR 2021
Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
JMLR 2021
Incorporating Unlabeled Data into Distributionally Robust Learning
JMLR 2021
Sparse-to-Dense Feature Matching: Intra and Inter Domain Cross-Modal Learning in Domain Adaptation for 3D Semantic Segmentation
ICCV 2021
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
ICCV 2021
Self-Supervised Monocular Depth Estimation for All Day Images Using Domain Separation
ICCV 2021
Testing Using Privileged Information by Adapting Features With Statistical Dependence
ICCV 2021
An Entity-Aware Adversarial Domain Adaptation Network for Cross-Domain Named Entity Recognition (Student Abstract)
AAAI 2021
Designing Transportable Experiments Under S-admissability
AISTATS 2021
Counterfactual Representation Learning with Balancing Weights
AISTATS 2021
Pareto Domain Adaptation
NIPS 2021
Rethinking Neural Operations for Diverse Tasks
NIPS 2021
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
NIPS 2021
Recovering Latent Causal Factor for Generalization to Distributional Shifts
NIPS 2021
An Information-theoretic Approach to Distribution Shifts
NIPS 2021
Federated Graph Classification over Non-IID Graphs
NIPS 2021
Training and Domain Adaptation for Supervised Text Segmentation
EACL 2021
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