Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 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/biomedia-mira/masf
공식 코드Domain Generalization via Model-Agnostic Learning of Semantic Features
https://github.com/biqiwhu/hssh
공식 코드Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination
https://github.com/bismex/MetaBIN
공식 코드Meta Batch-Instance Normalization for Generalizable Person Re-Identification
https://github.com/bit-da/cirl
공식 코드Causality Inspired Representation Learning for Domain Generalization
https://github.com/bit-da/dcg
공식 코드https://github.com/bit-ml/Stylist
공식 코드Robust Novelty Detection through Style-Conscious Feature Ranking
https://github.com/bit-ml/dupin
공식 코드https://github.com/boschresearch/aldm
공식 코드Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive
https://github.com/boschresearch/issa
공식 코드Intra-Source Style Augmentation for Improved Domain Generalization
https://github.com/boyanglyu/dg_via_wb
공식 코드Barycentric-alignment and reconstruction loss minimization for domain generalization
https://github.com/brucewlee/prompt-learning-readability
공식 코드https://github.com/busername/learning_to_diversify
공식 코드https://github.com/c-juhwan/unigen
공식 코드https://github.com/c0notsilly/advfrequency
공식 코드Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
https://github.com/camma-public/surglatentgraph
공식 코드Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer
https://github.com/capybaralet/REx_code_release
공식 코드Out-of-Distribution Generalization via Risk Extrapolation (REx)
https://github.com/casl-ku/heteroswitch
공식 코드HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
https://github.com/cbg-ethz/bmi
공식 코드Beyond Normal: On the Evaluation of Mutual Information Estimators
https://github.com/ccj5351/MS-Nets
공식 코드Matching-space Stereo Networks for Cross-domain Generalization
https://github.com/ccxu-ustc/sam
공식 코드https://github.com/changdaeoh/farconvae
공식 코드Learning Fair Representation via Distributional Contrastive Disentanglement
https://github.com/chehx/dgdr
공식 코드Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains
https://github.com/chen-ziyang/trid
공식 코드https://github.com/cheng-01037/causality-medical-image-domain-generalization
공식 코드Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
https://github.com/chengcv/dfrfs
공식 코드Disentangled Feature Representation for Few-shot Image Classification
https://github.com/chengyui/nuwats
공식 코드NuwaTS: a Foundation Model Mending Every Incomplete Time Series
https://github.com/chongqingnosubway/prompt-ot
공식 코드https://github.com/chris210634/mudg
공식 코드Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap
https://github.com/chris210634/word_soups
공식 코드https://github.com/chumsy0725/fbc-sa
공식 코드https://github.com/chumsy0725/spsd-vit
공식 코드Generalizing to Unseen Domains in Diabetic Retinopathy Classification
https://github.com/cianeastwood/qrm
공식 코드Probable Domain Generalization via Quantile Risk Minimization
https://github.com/clovaai/CutMix-PyTorch
공식 코드CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/cmry/amica
공식 코드https://github.com/cnewbie007/DADG
공식 코드Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.