Multi-target Domain Adaptation
20 papers with code • 4 benchmarks • 4 datasets
The idea of Multi-target Domain Adaptation is to adapt a model from a single labelled source domain to multiple unlabelled target domains.
Libraries
Use these libraries to find Multi-target Domain Adaptation models and implementations
Office-Home
DomainNet
Office-31
OBJ-MDA
Most implemented papers
Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications.
A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training
fpv-iplab/STMDA-RetinaNet
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Computer Vision and Image Understanding (CVIU) 2022
Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices.
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns.
A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training
fpv-iplab/STMDA-RetinaNet
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Computer Vision and Image Understanding (CVIU) 2022
Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices.
Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
mael-zys/coast
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In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions.
CoNMix for Source-free Single and Multi-target Domain Adaptation
vcl-iisc/CoNMix
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The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing.
Cyclically Disentangled Feature Translation for Face Anti-spoofing
We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains.
Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Pengchengpcx/Class-overwhelms-Mutual-Conditional-Blended-Target-Domain-Adaptation
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We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes.
MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds
Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabelled target domain and labelled source domain.
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA.
