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/kevinlin311tw/CDCL-human-part-segmentation
공식 코드Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
https://github.com/kevinmusgrave/pytorch-adapt
공식 코드https://github.com/kexin-kxzhang/cd2-tsi
공식 코드Cross-Domain Conditional Diffusion Models for Time Series Imputation
https://github.com/keyu07/DADG
공식 코드Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
https://github.com/kezhang-cs/Video-Summarization-with-LSTM
공식 코드https://github.com/kgl-prml/Pixel-Level-Cycle-Association
공식 코드Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
https://github.com/khainb/BoMb-OT
공식 코드https://github.com/khanrc/swad
공식 코드https://github.com/khu-agi/bus
공식 코드https://github.com/khu-vll/glad
공식 코드https://github.com/khuvll/glad
공식 코드https://github.com/kiakh93/ot-regularized-uda
공식 코드Domain adaptation using optimal transport for invariant learning using histopathology datasets
https://github.com/kiananvari/statistical-distance-guided-unsupervised-domain-adaptation-for-automated-multi-class-cmr-iqa
공식 코드https://github.com/kilianFatras/JUMBOT
공식 코드Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
https://github.com/kimanki/tafas
공식 코드Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
https://github.com/king-haw/gms
공식 코드https://github.com/kinredon/umt
공식 코드https://github.com/kiwi12138/realistictta
공식 코드Unraveling Batch Normalization for Realistic Test-Time Adaptation
https://github.com/klean2050/eeg_crossmodal
공식 코드https://github.com/kli-lalala/Dual-Teacher-
공식 코드https://github.com/kohpangwei/group_DRO
공식 코드https://github.com/kojima-takeshi188/cfa
공식 코드https://github.com/koncle/TSMLDG
공식 코드Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization
https://github.com/koncle/domainadaptor
공식 코드https://github.com/koncle/mvdg
공식 코드MVDG: A Unified Multi-view Framework for Domain Generalization
https://github.com/korawat-tanwisuth/proto_da
공식 코드A Prototype-Oriented Framework for Unsupervised Domain Adaptation
https://github.com/kota-dohi/dcase2022_task2_baseline_ae
공식 코드https://github.com/kota-dohi/dcase2022_task2_baseline_mobile_net_v2
공식 코드https://github.com/koutilya40192/SharinGAN
공식 코드SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
https://github.com/kowshikthopalli/DREAME
공식 코드Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization
https://github.com/kowshikthopalli/cattan
공식 코드https://github.com/kowshikthopalli/sista
공식 코드Single-Shot Domain Adaptation via Target-Aware Generative Augmentation
https://github.com/koyejolab/fweg
공식 코드Optimizing Black-box Metrics with Iterative Example Weighting
https://github.com/kpeng9510/ebil-hads
공식 코드Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
https://github.com/kpeng9510/featfsda
공식 코드Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.