Domain Adaptation On Imageclef Da

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
CMKD	94.3	Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
MCC+NWD	90.7	Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
GLOT-DR	90.4	Global-Local Regularization Via Distributional Robustness
SPL	90.3	Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
DFA-SAFN	90.2	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
DADA	89.3	Dual Adversarial Domain Adaptation
DFA-ENT	89.1	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
DDA	88.9	Transfer Learning with Dynamic Distribution Adaptation
MEDM	88.9	Entropy Minimization vs. Diversity Maximization for Domain Adaptation
IAFN+ENT	88.9	Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
ASAN	88.6	Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks
CADA-P	88.3	Attending to Discriminative Certainty for Domain Adaptation
EasyTL	88.2	Easy Transfer Learning By Exploiting Intra-domain Structures
rRevGrad+CAT	80.7	Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
IDDA (Alexnet)	80.6	Looking back at Labels: A Class based Domain Adaptation Technique
CAADA	80.2	Correlation-aware Adversarial Domain Adaptation and Generalization
DAN	76.9	Learning Transferable Features with Deep Adaptation Networks

0 of 17 row(s) selected.

Domain Adaptation On Imageclef Da | SOTA | HyperAI