Command Palette
Search for a command to run...
Domain Adaptation On Office Caltech
Métriques
Average Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| SPL | 93 | Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling |
| MEDA[[Wang et al.2018]] | 92.8 | Visual Domain Adaptation with Manifold Embedded Distribution Alignment |
| CAPLS [[Wang, Bu, and Breckon2019]] | 91.8 | Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition |
| DAN[[Long et al.2015]] | 90.1 | Learning Transferable Features with Deep Adaptation Networks |
| JGSA[[Zhang, Li, and Ogunbona2017]] | 90.0 | Joint Geometrical and Statistical Alignment for Visual Domain Adaptation |
| DDC[[Tzeng et al.2014]] | 88.2 | Deep Domain Confusion: Maximizing for Domain Invariance |
| SCA[[Ghifary et al.2016]] | 85.9 | Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization |
| CORAL[[Sun, Feng, and Saenko2017]] | 84.7 | Correlation Alignment for Unsupervised Domain Adaptation |
0 of 8 row(s) selected.