https://github.com/KU-CVLAB/MoA
공식 코드Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters
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://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/KaiyangZhou/Dassl.pytorch
공식 코드https://github.com/KaiyangZhou/mixstyle-release
공식 코드https://github.com/KaiyangZhou/ssdg-benchmark
공식 코드Semi-Supervised Domain Generalization with Stochastic StyleMatch
https://github.com/LL-a-VO/VLLaVO
공식 코드https://github.com/LinusWu/TENET_Training
공식 코드Group-wise Inhibition based Feature Regularization for Robust Classification
https://github.com/MLforHealth/CausalDA
공식 코드Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
https://github.com/MLforHealth/ClinicalDG
공식 코드An Empirical Framework for Domain Generalization in Clinical Settings
https://github.com/Maddy12/SSL4VideoSurvey
공식 코드https://github.com/MediaBrain-SJTU/FACT
공식 코드https://github.com/Meowuu7/AutoGPart
공식 코드AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation
https://github.com/NVlabs/CSG
공식 코드https://github.com/NVlabs/STL
공식 코드Fully Attentional Networks with Self-emerging Token Labeling
https://github.com/NVlabs/weakly-supervised-gaze
공식 코드https://github.com/Newbeeer/TRM
공식 코드Learning Representations that Support Robust Transfer of Predictors
https://github.com/PKU-EPIC/GAPartNet
공식 코드https://github.com/Pei-KaiHuang/LDCformer_Ext
공식 코드Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing
https://github.com/Pulkit-Khandelwal/medical-mldg-seg
공식 코드Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging
https://github.com/QData/TextAttack-A2T
공식 코드https://github.com/ReijiSoftmaxSaito/ADSI
공식 코드Domain Generalization through Attenuation of Domain-Specific Information
https://github.com/RoyalVane/TAF-Cal
공식 코드Test-time Fourier Style Calibration for Domain Generalization
https://github.com/SeanLeng1/SCMD
공식 코드https://github.com/ShawMask/congater_dcase2022t1
공식 코드Domain Information Control at Inference Time for Acoustic Scene Classification
https://github.com/ShishuaiHu/DCAC
공식 코드https://github.com/SimonThomine/DBFAD
공식 코드https://github.com/SimonThomine/FABLE
공식 코드https://github.com/SiyuanHuang95/SUG
공식 코드SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
https://github.com/SonyCSLParis/ssl-singer-identity
공식 코드Singer Identity Representation Learning using Self-Supervised Techniques
https://github.com/VDIGPKU/OpenAD
공식 코드OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
https://github.com/VideoObjectSearch/RandPerson
공식 코드https://github.com/WANGXinyiLinda/CMLE
공식 코드Counterfactual Maximum Likelihood Estimation for Training Deep Networks
https://github.com/WoojuLee24/OA-DG
공식 코드https://github.com/XThomasBU/GUIDE
공식 코드What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization
https://github.com/XingangPan/IBN-Net
공식 코드Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/Xuxiusheng/BatStyler
공식 코드BatStyler: Advancing Multi-category Style Generation for Source-free Domain Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.