Papers with Code 아카이브

Multi-target Domain Adaptation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 4
보존된 평가 기록: 14

The idea of Multi-target Domain Adaptation is to adapt a model from a single labelled source domain to multiple unlabelled target domains.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Evgeneus/Graph-Domain-Adaptaion

공식 코드

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

https://github.com/LIVIAETS/MT-MTDA

공식 코드

Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation

https://github.com/Natlem/M-HTCN

공식 코드

Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer

https://github.com/Pengchengpcx/Class-overwhelms-Mutual-Conditional-Blended-Target-Domain-Adaptation

공식 코드

Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation

https://github.com/Seung-Hun-Lee/ADAS

공식 코드

ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation

https://github.com/VisionLearningGroup/DAL

공식 코드

Domain Agnostic Learning with Disentangled Representations

https://github.com/aleemsidra/convlora

공식 코드

ConvLoRA and AdaBN based Domain Adaptation via Self-Training

https://github.com/darrenjkt/SEE-MTDA

공식 코드

See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation

https://github.com/darrenjkt/SEE-VCN

공식 코드

Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

https://github.com/ewsn1593/hdrq

공식 코드

Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation

https://github.com/fpv-iplab/STMDA-RetinaNet

공식 코드

A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training

https://github.com/fremigereau/mtda_kd_reid

공식 코드

Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification

https://github.com/lkpengcs/rvms

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Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation

https://github.com/mael-zys/coast

공식 코드

Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation

https://github.com/munish30monga/COSMo

공식 코드

COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation

https://github.com/sinashish/mensa_mtda

공식 코드

MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds

https://github.com/thuml/Versatile-Domain-Adaptation

공식 코드

Minimum Class Confusion for Versatile Domain Adaptation

https://github.com/ut-vision/FgSty-CPL

공식 코드

Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation

https://github.com/vcl-iisc/CoNMix

공식 코드

CoNMix for Source-free Single and Multi-target Domain Adaptation

https://github.com/vis-face/cdftn

공식 코드

Cyclically Disentangled Feature Translation for Face Anti-spoofing

https://github.com/zjy526223908/BTDA

공식 코드

Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks

https://github.com/BalrajAshwath/Adversarial-Domain_Adaptation_Paper_Implementations

Unsupervised Domain Adaptation by Backpropagation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.