Papers with Code 아카이브
Domain Adaptation
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zzzqzhou/dual-normalization
공식 코드https://github.com/zzzx1224/A-Bit-More-Bayesian
공식 코드A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
https://github.com/zzzx1224/beyond-model-adaptation-at-test-time-papers
공식 코드https://github.com/zzzx1224/ebtsa-iclr2023
공식 코드Energy-Based Test Sample Adaptation for Domain Generalization
https://github.com/zzzx1224/singlesamplegeneralization-iclr2022
공식 코드Learning to Generalize across Domains on Single Test Samples
https://gitlab.com/cian.unibas.ch/L2I
공식 코드Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis
https://gitlab.com/dlr-dw/type-inference
공식 코드Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
https://gitlab.com/tringwald/cvp
공식 코드Certainty Volume Prediction for Unsupervised Domain Adaptation
https://gitlab.com/tringwald/ubr2s
공식 코드UBR$^2$S: Uncertainty-Based Resampling and Reweighting Strategy for Unsupervised Domain Adaptation
https://gitlab.com/tringwald/ufal
공식 코드Unsupervised Domain Adaptation by Uncertain Feature Alignment
https://gitlab.kitware.com/darpa_learn/learn
공식 코드LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning
https://huggingface.co/sparks-solutions/ELAM-7B
공식 코드Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UI
https://openi.pcl.ac.cn/OpenMedIA/MAKEN
공식 코드https://worksheets.codalab.org/worksheets/0x2613c72d4f3f4fbb94e0a32c17ce5fb0
공식 코드https://worksheets.codalab.org/worksheets/0x621811fe446b49bb818293bae2ef88c0
공식 코드https://worksheets.codalab.org/worksheets/0xabfe206d857d40a5baf2a19ece5f2659
공식 코드https://bitbucket.org/skeizer/madrigal
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling
https://github.com/04RR/SOTA-Vision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/0jason000/convnext
https://github.com/0jason000/mae_vit
https://github.com/2020132075/conmae
https://github.com/2023-MindSpore-1/ms-code-123
https://github.com/2023-MindSpore-1/ms-code-13
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/autoaugment
https://github.com/2023-MindSpore-1/ms-code-177
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-206
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/CMT
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.