Domain Adaptation On Office Caltech
Métriques
Average Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average Accuracy | Paper Title | Repository |
|---|---|---|---|
| SCA[[Ghifary et al.2016]] | 85.9 | Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization | - |
| JGSA[[Zhang, Li, and Ogunbona2017]] | 90.0 | Joint Geometrical and Statistical Alignment for Visual Domain Adaptation | - |
| MEDA[[Wang et al.2018]] | 92.8 | Visual Domain Adaptation with Manifold Embedded Distribution Alignment | |
| SPL | 93 | Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling | |
| DDC[[Tzeng et al.2014]] | 88.2 | Deep Domain Confusion: Maximizing for Domain Invariance | |
| DAN[[Long et al.2015]] | 90.1 | Learning Transferable Features with Deep Adaptation Networks | |
| CAPLS [[Wang, Bu, and Breckon2019]] | 91.8 | Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition | |
| CORAL[[Sun, Feng, and Saenko2017]] | 84.7 | Correlation Alignment for Unsupervised Domain Adaptation |
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