https://github.com/mainaksingha01/odg-clip
공식 코드Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
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/mancinimassimiliano/CuMix
공식 코드https://github.com/mancinimassimiliano/adagraph
공식 코드Towards Recognizing New Semantic Concepts in New Visual Domains
https://github.com/mancinimassimiliano/caffe
공식 코드https://github.com/manveertamber/enhancing_domain_adaptation
공식 코드Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
https://github.com/marclafon/gallop
공식 코드GalLoP: Learning Global and Local Prompts for Vision-Language Models
https://github.com/marrlab/domainlab
공식 코드https://github.com/maryam089/sdvit
공식 코드https://github.com/masseeh/dcaug
공식 코드https://github.com/mawanqin2002/stacom2022ma
공식 코드https://github.com/mechrev0/mudpt
공식 코드MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language Models
https://github.com/mediabrain-sjtu/disam
공식 코드Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
https://github.com/megvii-research/simpledg
공식 코드SimpleDG: Simple Domain Generalization Baseline without Bells and Whistles
https://github.com/mehrdad-noori/fds
공식 코드https://github.com/mehrdad-noori/tfs-vit_token-level_feature_stylization
공식 코드TFS-ViT: Token-Level Feature Stylization for Domain Generalization
https://github.com/meilfang/cf-pad
공식 코드Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics
https://github.com/meilfang/synthaspoof
공식 코드SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
https://github.com/mengfeid/panoos
공식 코드https://github.com/metavisionlab/ddg
공식 코드https://github.com/metavisionlab/pe
공식 코드https://github.com/metavisionlab/ssdg
공식 코드Semi-Supervised Domain Generalization with Evolving Intermediate Domain
https://github.com/mfederici/dsit
공식 코드https://github.com/mhealthbuet/domainagnosticcxr
공식 코드https://github.com/miccunifi/kdpl
공식 코드Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation
https://github.com/microsoft/S2R-DepthNet
공식 코드S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/microsoft/SNR
공식 코드Style Normalization and Restitution for Generalizable Person Re-identification
https://github.com/microsoft/robustdg
공식 코드https://github.com/mil-tokyo/dg_mmld
공식 코드Domain Generalization Using a Mixture of Multiple Latent Domains
https://github.com/mingukjang/tast
공식 코드Test-Time Adaptation via Self-Training with Nearest Neighbor Information
https://github.com/miralab-ustc/ai4logicsynthesis-prunex
공식 코드A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
https://github.com/mit-ll/robust-vision-language-finetuning
공식 코드Robust Fine-Tuning of Vision-Language Models for Domain Generalization
https://github.com/mlfoundations/model-soups
공식 코드https://github.com/mlfoundations/tableshift
공식 코드Benchmarking Distribution Shift in Tabular Data with TableShift
https://github.com/mlmed/torchxrayvision
공식 코드On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/mlvlab/prometar
공식 코드https://github.com/mlvlab/rpo
공식 코드Read-only Prompt Optimization for Vision-Language Few-shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.