https://github.com/valeoai/ttyd
공식 코드Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/valeoai/xmuda
공식 코드xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
https://github.com/valeoai/xmuda_journal
공식 코드Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
https://github.com/valeriopaolicelli/AdAGeo
공식 코드Adaptive-Attentive Geolocalization from few queries: a hybrid approach
https://github.com/valeriopaolicelli/adageo-WACV2021
공식 코드Adaptive-Attentive Geolocalization from few queries: a hybrid approach
https://github.com/valterlej/zsarcap
공식 코드Tell me what you see: A zero-shot action recognition method based on natural language descriptions
https://github.com/vandadim/adas_mri
공식 코드Comparison of single and multitask learning for predicting cognitive decline based on MRI data
https://github.com/vannguyennd/dam2p
공식 코드Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle
https://github.com/vcl-iisc/CoNMix
공식 코드CoNMix for Source-free Single and Multi-target Domain Adaptation
https://github.com/vcl-iisc/data-free-defense-at-test-time
공식 코드https://github.com/vcl-iisc/improved-data-free-test-time-adversarial-defense
공식 코드https://github.com/velodc/oshot_detection
공식 코드Exploring Data Aggregation and Transformations to Generalize across Visual Domains
https://github.com/velor2012/tdo-dif
공식 코드Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion
https://github.com/venkatesh-saligrama/pac
공식 코드Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
https://github.com/vibashan/irg-sfda
공식 코드Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
https://github.com/vibashan/memxformer-online-da
공식 코드https://github.com/vibashan/online-od
공식 코드https://github.com/vibashan/tt-sfuda
공식 코드Target and Task specific Source-Free Domain Adaptive Image Segmentation
https://github.com/vidit09/domaingen
공식 코드CLIP the Gap: A Single Domain Generalization Approach for Object Detection
https://github.com/vidurj/parser-adaptation
공식 코드Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
https://github.com/viethoang1512/glot
공식 코드https://github.com/viethoang1512/pga
공식 코드Enhancing Domain Adaptation through Prompt Gradient Alignment
https://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/vikolss/DACS
공식 코드https://github.com/vilab-ucsd/udabench_eccv2024
공식 코드https://github.com/vimar-gu/Bias-Eliminate-DA-ReID
공식 코드1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification
https://github.com/viml-cvdl/object-detection-in-foggy-scenes
공식 코드Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
https://github.com/vinairesearch/mdsdi
공식 코드Exploiting Domain-Specific Features to Enhance Domain Generalization
https://github.com/virajprabhu/SENTRY
공식 코드SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
https://github.com/virajprabhu/clue
공식 코드Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
https://github.com/virajprabhu/pacmac
공식 코드Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
https://github.com/vis-face/cdftn
공식 코드Cyclically Disentangled Feature Translation for Face Anti-spoofing
https://github.com/visinf/da-sac
공식 코드Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.