https://github.com/sshan-zhao/GASDA
공식 코드Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation
Texture Learning Domain Randomization for Domain Generalized Segmentation
Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization
Frequency Tracking Features for Data-Efficient Deep Siren Identification
Data Centric Domain Adaptation for Historical Text with OCR Errors
Spot the Difference: Detection of Topological Changes via Geometric Alignment
Unsupervised Online Continual Learning for Automatic Speech Recognition
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
Investigating Generalization in Neural Networks under Optimally Evolved Training Perturbations
WildNet: Learning Domain Generalized Semantic Segmentation from the Wild
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Direct domain adaptation through reciprocal linear transformations
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.