Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/YugeTen/fish
공식 코드https://github.com/Yuliang-Liu/VimTS
공식 코드VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization
https://github.com/Yux1angJi/DIFF
공식 코드Diffusion Features to Bridge Domain Gap for Semantic Segmentation
https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios
공식 코드https://github.com/ZitongYu/DeepFAS
공식 코드https://github.com/aamixsh/eeg_mci
공식 코드https://github.com/abhi460729/GATN
공식 코드https://github.com/adithya7/xlel-wd
공식 코드https://github.com/adnan-khan7/uplm
공식 코드Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
https://github.com/adobe/antialiased-cnns
공식 코드https://github.com/adragnar/irm-toxicity-classification
공식 코드Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification
https://github.com/aeolusguan/NMRF
공식 코드https://github.com/ai-application-and-integration-lab/dgua_fas
공식 코드https://github.com/aim-uofa/omni-r1
공식 코드Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
https://github.com/aimagelab/open-fashion-clip
공식 코드OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
https://github.com/airl-iisc/MRFP
공식 코드https://github.com/airlabkhu/a2xp
공식 코드https://github.com/akshaymehra24/CertifiableDG
공식 코드On Certifying and Improving Generalization to Unseen Domains
https://github.com/akshaymehra24/LimitsOfUDA
공식 코드Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
https://github.com/akshaymehra24/riskaversedg
공식 코드On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
https://github.com/albert0147/onering
공식 코드OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
https://github.com/alceubissoto/artifact-generalization-skin
공식 코드https://github.com/alexanderjaus/PPS
공식 코드https://github.com/alexrame/fishr
공식 코드Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
https://github.com/alexthebad/ap10k
공식 코드https://github.com/alfassy/FETA
공식 코드FETA: Towards Specializing Foundation Models for Expert Task Applications
https://github.com/alibaba/easyrobust
공식 코드https://github.com/alibaba/easyrobust/tree/main/examples/imageclassification/imagenet/dat
공식 코드Enhance the Visual Representation via Discrete Adversarial Training
https://github.com/alinlab/MASKER
공식 코드MASKER: Masked Keyword Regularization for Reliable Text Classification
https://github.com/alsace08/meta-reasoning
공식 코드Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models
https://github.com/alusi123/dfa
공식 코드https://github.com/ambekarsameer96/generalizeformer
공식 코드GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution Shifts
https://github.com/amitrozner/domain-generalizable-multiple-domain-clustering
공식 코드https://github.com/amodas/PRIME-augmentations
공식 코드PRIME: A few primitives can boost robustness to common corruptions
https://github.com/amy-hyunji/lora-for-retrieval
공식 코드Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.