https://bitbucket.org/aswathyve/unifieddeceptioncorpus
공식 코드UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 21
- 보존된 평가 기록
- 570
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://bitbucket.org/rskothari/multiset_gaze
공식 코드EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking
https://github.com/00why00/pfcd
공식 코드Prompt-Free Conditional Diffusion for Multi-object Image Augmentation
https://github.com/AMLab-Amsterdam/DIVA
공식 코드https://github.com/AMLab-Amsterdam/DataAugmentationInterventions
공식 코드https://github.com/AlexTheBad/AP-10K
공식 코드https://github.com/BIT-DA/DDA
공식 코드https://github.com/BiQiWHU/CMFormer
공식 코드Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
https://github.com/BorealisAI/cross_domain_coherence
공식 코드https://github.com/Boyiliee/MoEx
공식 코드https://github.com/ByeongHyunPak/tqdm
공식 코드Textual Query-Driven Mask Transformer for Domain Generalized Segmentation
https://github.com/Chumsy0725/DGWM
공식 코드Domain-Guided Weight Modulation for Semi-Supervised Domain Generalization
https://github.com/DarioFontanel/OWR-VisualDomains
공식 코드On the Challenges of Open World Recognitionunder Shifting Visual Domains
https://github.com/DenisPushkin/GOTU-real-valued
공식 코드On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
https://github.com/Erosinho13/FedDrive
공식 코드FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
https://github.com/GT-RIPL/DomainGeneralization-Stylization
공식 코드Frustratingly Simple Domain Generalization via Image Stylization
https://github.com/Gahyeonkim09/AAPL
공식 코드AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
https://github.com/GauriGupta19/Domain-Generalisation-in-Invariance
공식 코드https://github.com/GongShuai8210/PLAN
공식 코드Federated Domain Generalization via Prompt Learning and Aggregation
https://github.com/HAHA-DL/Episodic-DG
공식 코드https://github.com/HaohanWang/PAR
공식 코드Learning Robust Global Representations by Penalizing Local Predictive Power
https://github.com/HaohanWang/PAR_experiments
공식 코드Learning Robust Global Representations by Penalizing Local Predictive Power
https://github.com/HaokunChen245/DFDG
공식 코드https://github.com/Haoqing-Wang/CDFSL-ATA
공식 코드Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
https://github.com/HardworkingPearl/EDG-DDA
공식 코드https://github.com/HeliosZhao/M3L
공식 코드https://github.com/HzFu/COVID19_imaging_AI_paper_list
공식 코드Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
https://github.com/IBM/OoD
공식 코드Treatment Effect Estimation using Invariant Risk Minimization
https://github.com/JabezNzomo99/claim-matching-robustness
공식 코드https://github.com/JeremyCJM/CCST
공식 코드Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer
https://github.com/JigglypuffStitch/AdaFreq
공식 코드Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification
https://github.com/JingWang18/Discriminative-Feature-Alignment
공식 코드https://github.com/JingbiaoMei/RGCL
공식 코드Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
https://github.com/Jonas1302/anomalib
공식 코드https://github.com/JudgingH/PEGO
공식 코드https://github.com/KDongYoung/Calibration-Free-Driver-Drowsiness-Classification-based-on-Manifold-Level-Augmentation
공식 코드Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.