XDC: Adversarial Adaptive Cross Domain Face Clustering (Student Abstract)


  • Saed Rezayi University of Georgia
  • Handong Zhao Adobe Research
  • Sheng Li University of Georgia




Domain Adaptation, Adversarial Learning, Face Clustering, Cross-domain Clustering


In this work we propose a scheme, called XDC, that uses adversarial learning to train an adaptive cross domain clustering model. XDC trains a classifier on a labeled dataset and assigns labels to an unlabeled dataset. We benefit from adversarial learning such that the target dataset takes part in the training. We also use an existing image classifiers in a plug-and-play fashion (i.e, it can be replaced with any other image classifier). Unlike existing works we update the parameters of the encoder and expose the target dataset to the model during training. We apply our model on two face dataset and one non-face dataset and obtain comparable results with state-of-the-art face clustering models.




How to Cite

Rezayi, S., Zhao, H., & Li, S. (2022). XDC: Adversarial Adaptive Cross Domain Face Clustering (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13035-13036. https://doi.org/10.1609/aaai.v36i11.21654