Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator... (read more)

PDF Abstract CVPR 2018 PDF CVPR 2018 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Domain Adaptation HMDBfull-to-UCF MCD Accuracy 79.34 # 3
Domain Adaptation MNIST-to-USPS MCD Accuracy 93.8 # 9
Synthetic-to-Real Translation Syn2Real-C MCD Accuracy 71.9 # 4
Domain Adaptation SYNSIG-to-GTSRB MCD Accuracy 94.4 # 3
Domain Adaptation UCF-to-HMDBfull MCD Accuracy 73.89 # 4
Domain Adaptation USPS-to-MNIST MCD Accuracy 95.7 # 8

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Domain Adaptation SVHN-to-MNIST MCD Accuracy 95.8 # 5

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet