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/bhyun-kim/superpoint-jax
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/bigdata-ustc/rcd
https://github.com/bigdata-ustc/techcd
https://github.com/bigscience-workshop/Megatron-DeepSpeed
https://github.com/bigscience-workshop/promptsource
https://github.com/bimsarapathiraja/mccl
https://github.com/bioidiap/bob.ip.binseg
On the Evaluation and Real-World Usage Scenarios of Deep Vessel Segmentation for Retinography
https://github.com/blablabananarama/ukiyoGAN
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
https://github.com/blue-soda/samgpt
SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation
https://github.com/bobvo23/DANN-Plant-Counting
https://github.com/borglab/gtsfm
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/boris127/vehicle-detection
https://github.com/brycexu/MR-Residual-Net
https://github.com/bshantam97/Attention_Based_Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/bumuckl/SemiSupervisedDLForFCNs
Semi-Supervised Deep Learning for Fully Convolutional Networks
https://github.com/bunsenfeng/factkb
MultiVerS: Improving scientific claim verification with weak supervision and full-document context
https://github.com/bupt-ai-cz/hhcl-reid
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/buriedms/AdaptSegNet-Paddle
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/buyeah1109/KEN
https://github.com/buyeah1109/finc
https://github.com/bwconrad/masked-autoencoder
https://github.com/bytedance/neurst
Finding Sparse Structures for Domain Specific Neural Machine Translation
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/caesarea38/doclangid
https://github.com/caiqi/mean-teacher-cross-domain-detection
Exploring Object Relation in Mean Teacher for Cross-Domain Detection
https://github.com/calico/scnym
https://github.com/calmisential/TensorFlow2.0_Image_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/cameronchoi/r3det-copy
https://github.com/canqin001/UODA
Contradictory Structure Learning for Semi-supervised Domain Adaptation
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/caoquanjie/ADDA-master
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.