Papers with Code 아카이브

Unsupervised Domain Adaptation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 49
보존된 평가 기록: 393

Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.

Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation

코드

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

https://github.com/chenhao2345/ucr

공식 코드

Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal

https://github.com/chenxi52/upa

공식 코드

Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation

https://github.com/chingyaoc/mdm

공식 코드

The Role of Embedding Complexity in Domain-invariant Representations

https://github.com/chma1024/densepass

공식 코드

DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange

https://github.com/chrisallenming/cross_category_video_highlight

공식 코드

Cross-category Video Highlight Detection via Set-based Learning

https://github.com/chrisantuseze/active-self-pretraining

공식 코드

A3: Active Adversarial Alignment for Source-Free Domain Adaptation

https://github.com/christianabbet/sra

공식 코드

Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection

https://github.com/ckarouzos/slp_daptmlm

공식 코드

UDALM: Unsupervised Domain Adaptation through Language Modeling

https://github.com/cliffbb/uda-nas

공식 코드

Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping

https://github.com/cmhungsteve/TA3N

공식 코드

Temporal Attentive Alignment for Large-Scale Video Domain Adaptation

https://github.com/cobypenso/unsupervised-target-domain-calibration

공식 코드

Calibration of Network Confidence for Unsupervised Domain Adaptation Using Estimated Accuracy

https://github.com/collin-burns/feature-alignment

공식 코드

Limitations of Post-Hoc Feature Alignment for Robustness

https://github.com/congxin95/DaFeC

공식 코드

Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

https://github.com/cscyqj/miccai23-protocontra-sfda

공식 코드

Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

https://github.com/cswin/AWC

공식 코드

CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

https://github.com/cswin/cada

공식 코드

CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

https://github.com/ctu-vras/t-uda

공식 코드

T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds

https://github.com/cuishuhao/GVB

공식 코드

Gradually Vanishing Bridge for Adversarial Domain Adaptation

https://github.com/cv-rits/densemtl

공식 코드

DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning

https://github.com/cv-shuchanglyu/sam-joanet

공식 코드

Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model

https://github.com/cvi2snt/dda-mlic

공식 코드

Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification

https://github.com/cvlab-unibo/d4-dbst

공식 코드

Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation

https://github.com/cvlab-unibo/mm2d3d

공식 코드

Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation

https://github.com/cvlab-unibo/refrec

공식 코드

RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

https://github.com/cvlab-unibo/shallow_da

공식 코드

Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries

https://github.com/cvmi-lab/doda

공식 코드

DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation

https://github.com/cxa9264/confeti

공식 코드

Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation

https://github.com/cyang-cityu/MetaCorrection

공식 코드

MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

https://github.com/cynthpie/synthia_melody

공식 코드

Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio

https://github.com/daintlab/fewshot-sfda

공식 코드

Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation

https://github.com/danifranco/em_domain_adaptation

공식 코드

Deep learning based domain adaptation for mitochondria segmentation on EM volumes

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

공식 코드

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

https://github.com/darrenjkt/ms3d

공식 코드

MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection

https://github.com/data-designer/da

공식 코드

Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

https://github.com/davidpujol/SADA

공식 코드

SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization

https://github.com/dazhangyu123/HCRPL

공식 코드

Hard Class Rectification for Domain Adaptation

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