https://github.com/utahnlp/marginal_srl_with_semlink
공식 코드Learning Semantic Role Labeling from Compatible Label Sequences
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/uw-exp/globem
공식 코드GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
https://github.com/vaibhav-khamankar/fusestyle
공식 코드https://github.com/val-iisc/SFDA-Seg
공식 코드Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
https://github.com/val-iisc/VL2V-ADiP
공식 코드Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
https://github.com/val-iisc/dajat
공식 코드Efficient and Effective Augmentation Strategy for Adversarial Training
https://github.com/val-iisc/dart
공식 코드DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
https://github.com/velodc/oshot_detection
공식 코드Exploring Data Aggregation and Transformations to Generalize across Visual Domains
https://github.com/vidit09/domaingen
공식 코드CLIP the Gap: A Single Domain Generalization Approach for Object Detection
https://github.com/viethoang1512/glot
공식 코드https://github.com/vihari/cgd
공식 코드Focus on the Common Good: Group Distributional Robustness Follows
https://github.com/vihari/crossgrad
공식 코드https://github.com/vihari/csd
공식 코드Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
https://github.com/viictorjimenezzz/pa-covariate-shift
공식 코드Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
https://github.com/vinairesearch/mdsdi
공식 코드Exploiting Domain-Specific Features to Enhance Domain Generalization
https://github.com/visinf/self-adaptive
공식 코드Semantic Self-adaptation: Enhancing Generalization with a Single Sample
https://github.com/visionlearninggroup/benchmark_domain_transfer
공식 코드A Broad Study of Pre-training for Domain Generalization and Adaptation
https://github.com/visionxlab/earth-adapter
공식 코드Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation
https://github.com/visipedia/caltech-fish-counting
공식 코드The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
https://github.com/vitae-transformer/aptv2
공식 코드APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and Beyond
https://github.com/vitae-transformer/p3m-net
공식 코드https://github.com/vorobeevich/distillation-in-dg
공식 코드Weight Averaging Improves Knowledge Distillation under Domain Shift
https://github.com/vtddggg/Robust-Vision-Transformer
공식 코드https://github.com/w1oves/rein
공식 코드https://github.com/wang-pengfei/sagm
공식 코드https://github.com/wangxinyilinda/causal-balancing-for-domain-generalization
공식 코드https://github.com/wangyiqi/pmpo
공식 코드https://github.com/wangzhuo2019/ssan
공식 코드Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
https://github.com/wbrun0/LawngNLI
공식 코드https://github.com/wellecks/naturalproofs
공식 코드NaturalProofs: Mathematical Theorem Proving in Natural Language
https://github.com/wenbowen123/catgrasp
공식 코드CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
https://github.com/wenkehuang/fccl
공식 코드Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
https://github.com/wenruxue/detta
공식 코드https://github.com/wentaotan/interleaved-learning
공식 코드Style Interleaved Learning for Generalizable Person Re-identification
https://github.com/williamstoto/imo
공식 코드https://github.com/wjq-learning/sleepdg
공식 코드Generalizable Sleep Staging via Multi-Level Domain Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.