Domain Adaptation On Office 31

Metrics

Average Accuracy

Results

Performance results of various models on this benchmark

Model Name	Average Accuracy	Paper Title	Repository
d-SNE	81.63	Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol
DAOD	85.4	Open Set Domain Adaptation: Theoretical Bound and Algorithm
MRKLD + LRENT	86.8	Confidence Regularized Self-Training
CMKD	94.4	Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
DADA	89	Discriminative Adversarial Domain Adaptation
GTA	86.5	Generate To Adapt: Aligning Domains using Generative Adversarial Networks
PMTrans	95.3	Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective	-
CAADA	78.3	Correlation-aware Adversarial Domain Adaptation and Generalization	-
rRevGrad+CAT	80.1	Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
IDDA(Alexnet)	78.5	Looking back at Labels: A Class based Domain Adaptation Technique
SHOT	88.6	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
CDTrans	92.6	CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
FixBi	91.4	FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
BIWAA	90.5	Backprop Induced Feature Weighting for Adversarial Domain Adaptation with Iterative Label Distribution Alignment
SFDA2	89.9	SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
SRDA (RAN)	73.5	Learning Smooth Representation for Unsupervised Domain Adaptation
IDDA (AlexNet)	78.5	Looking back at Labels: A Class based Domain Adaptation Technique
dSNE	90.01	d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding
ELS	90.4	Free Lunch for Domain Adversarial Training: Environment Label Smoothing
ResNet-50	76.1	Deep Residual Learning for Image Recognition

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