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/zjsong/sspl
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/zkcys001/UDAStrongBaseline
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/zpc-666/Paddle-R-Drop
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/zphang/lm_evaluation_harness
https://github.com/zqwhu/SegDAwithBoundary
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/zsef123/EfficientNets-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/zuoym15/craves.ai
CRAVES: Controlling Robotic Arm with a Vision-based Economic System
https://github.com/zysymu/AdaMatch-pytorch
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/zzzace2000/robust_cls_model
https://gitlab.com/birder/birder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://gitlab.com/williamsdaniel888/valma
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://huggingface.co/lamm-mit/Llama3.1-8b-Instruct-CPT-SFT-ORPO-SLERP-09022024
https://huggingface.co/lamm-mit/SmolLM-Base-1.7B-CPT-SFT-DPO-09022024
https://huggingface.co/lamm-mit/leaf-FLUX.1-dev
https://huggingface.co/lamm-mit/leaf-L-FLUX.1-dev
https://huggingface.co/lamm-mit/mistral-7B-v0.3-Base-CPT-SFT-DPO-09022024
https://huggingface.co/lamm-mit/mistral-7B-v0.3-Base-CPT-SFT-SLERP-09022024
https://worksheets.codalab.org/worksheets/0xfafccca55b584e6eb1cf71979ad8e778
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.