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Supervised Domain Adaptation using Graph Embedding

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Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.
OriginalsprogEngelsk
Udgivelsesår10 jan. 2021
Antal sider7
StatusUdgivet - 10 jan. 2021
Begivenhed25th International Conference on Pattern Recognition - Milan, Italien
Varighed: 10 jan. 202115 jan. 2021
https://www.micc.unifi.it/icpr2020/

Konference

Konference25th International Conference on Pattern Recognition
LandItalien
ByMilan
Periode10/01/202115/01/2021
Internetadresse

    Forskningsområder

  • transfer learning, domain adaptation, graph embedding, deep learning

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