https://github.com/keyu07/DADG
공식 코드Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
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/khanrc/swad
공식 코드https://github.com/king-haw/gms
공식 코드https://github.com/kohpangwei/group_DRO
공식 코드https://github.com/koncle/TSMLDG
공식 코드Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization
https://github.com/koncle/mvdg
공식 코드MVDG: A Unified Multi-view Framework for Domain Generalization
https://github.com/kota-dohi/dcase2022_task2_baseline_ae
공식 코드https://github.com/kota-dohi/dcase2022_task2_baseline_mobile_net_v2
공식 코드https://github.com/kowshikthopalli/DREAME
공식 코드Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization
https://github.com/kpeng9510/ebil-hads
공식 코드Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
https://github.com/kpeng9510/hyprometa
공식 코드Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization
https://github.com/kunkunlin1221/InstructFLIP
공식 코드InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
https://github.com/lapisrocks/DiscreteAdversarialDistillation
공식 코드Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
https://github.com/lccurious/DRDA
공식 코드https://github.com/learningmatter-mit/uq_singlenn
공식 코드https://github.com/leejoonhun/fan-beats
공식 코드https://github.com/leekyungmoon/XDED
공식 코드Cross-Domain Ensemble Distillation for Domain Generalization
https://github.com/leminhbinh0209/cvpr24-fas
공식 코드https://github.com/leokarlin/brad
공식 코드Unsupervised Domain Generalization by Learning a Bridge Across Domains
https://github.com/leolyj/san-saw
공식 코드https://github.com/leon-francis/factual
공식 코드https://github.com/less-and-less-bugs/rdcm
공식 코드Robust Domain Misinformation Detection via Multi-modal Feature Alignment
https://github.com/leuchine/self_play_picard
공식 코드https://github.com/lhoyer/hrda
공식 코드https://github.com/liamheng/non-iid_medical_image_segmentation
공식 코드Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
https://github.com/liangchen527/causeb
공식 코드A Causal Inspired Early-Branching Structure for Domain Generalization
https://github.com/liangchen527/itta
공식 코드https://github.com/liangchen527/lfme
공식 코드LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
https://github.com/liangchen527/ridg
공식 코드https://github.com/lidar-perception/lidar-cs
공식 코드LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
https://github.com/lileicv/PDEN
공식 코드Progressive Domain Expansion Network for Single Domain Generalization
https://github.com/lilianngweta/pisco
공식 코드Simple Disentanglement of Style and Content in Visual Representations
https://github.com/lingeringlight/aloft
공식 코드https://github.com/lingeringlight/domaindrop
공식 코드DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
https://github.com/lingeringlight/placedropout
공식 코드PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
https://github.com/lingeringlight/seta
공식 코드SETA: Semantic-Aware Token Augmentation for Domain Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.