Unsupervised Domain Adaptation by Backpropagation

26 Sep 2014Yaroslav GaninVictor Lempitsky

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Unsupervised Image-To-Image Translation SVNH-to-MNIST DANN Classification Accuracy 73.6% # 4

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Domain Adaptation HMDBfull-to-UCF RevGrad Accuracy 74.44 # 4
Domain Adaptation HMDBsmall-to-UCF TemPooling + RevGrad Accuracy 98.41 # 2
Domain Adaptation Olympic-to-HMDBsmall TemPooling + RevGrad Accuracy 90.00 # 2
Domain Adaptation UCF-to-HMDBfull RevGrad Accuracy 74.44 # 3
Domain Adaptation UCF-to-HMDBsmall TemPooling + RevGrad Accuracy 99.33 # 1
Domain Adaptation UCF-to-Olympic TemPooling + RevGrad Accuracy 98.15 # 1
Transfer Learning Office-Home DANN Accuracy 57.6 # 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