https://github.com/ylily1015/dogetickets
공식 코드Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets
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/ynysjtu/ood_bench
공식 코드OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
https://github.com/yore0/ttdg-mgm
공식 코드https://github.com/ypwang61/one-shot-rlvr
공식 코드Reinforcement Learning for Reasoning in Large Language Models with One Training Example
https://github.com/ysj9909/DG-SoRA
공식 코드SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation Learning
https://github.com/yu-rp/distribution-shift-iverson
공식 코드Distribution Shift Inversion for Out-of-Distribution Prediction
https://github.com/yuan-12138/mi-segnet
공식 코드MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization
https://github.com/yuanjianhao508/notjustprettypictures
공식 코드Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image Generators
https://github.com/yuhangzang/upt
공식 코드https://github.com/yulearningg/LRDG
공식 코드Domain Generalization by Learning and Removing Domain-specific Features
https://github.com/yunfeixie233/vigal
공식 코드https://github.com/yunqing-me/Born-Again-FS
공식 코드FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification
https://github.com/yuqiu-rgb/uniptms
공식 코드https://github.com/yuweisunn/federated-knowledge-alignment
공식 코드Feature Distribution Matching for Federated Domain Generalization
https://github.com/yuwlong666/fsdg
공식 코드Fine-Grained Domain Generalization with Feature Structuralization
https://github.com/yuxin-dong/idm
공식 코드How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis
https://github.com/yvanyin/filldepth
공식 코드https://github.com/yxltya/pointdgmamba
공식 코드PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model
https://github.com/yyzharry/multi-domain-imbalance
공식 코드On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
https://github.com/zekun-cai/koodos
공식 코드https://github.com/zhangce01/dualadapter
공식 코드Enhancing Vision-Language Few-Shot Adaptation with Negative Learning
https://github.com/zhangce01/simnl
공식 코드Enhancing Vision-Language Few-Shot Adaptation with Negative Learning
https://github.com/zhangda1018/dpd
공식 코드Dynamic Proxy Domain Generalizes the Crowd Localization by Better Binary Segmentation
https://github.com/zhanghr2001/promptta
공식 코드PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
https://github.com/zhaohaoyu376/morestyle
공식 코드https://github.com/zhaohengyuan1/sct
공식 코드SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
https://github.com/zhaorui-tan/GMDG_cvpr2024
공식 코드Rethinking Multi-domain Generalization with A General Learning Objective
https://github.com/zhaorui-tan/L-Reg_NeurIPS24
공식 코드https://github.com/zhaorui-tan/gmdg
공식 코드Rethinking Multi-domain Generalization with A General Learning Objective
https://github.com/zhaowanqing/FCN-LP
공식 코드Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph
https://github.com/zhengzangw/DoPrompt
공식 코드https://github.com/zhihou7/batchformer
공식 코드BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
https://github.com/zhijin-ge/stm
공식 코드Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
https://github.com/zhmeishi/dg-nu
공식 코드https://github.com/zhyczy/fed-co2
공식 코드https://github.com/ziwei-niu/dg_for_media
공식 코드A Survey on Domain Generalization for Medical Image Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.