Unsupervised Deep Learning via Affinity Diffusion


  • Jiabo Huang Queen Mary University of London
  • Qi Dong Queen Mary University of London
  • Shaogang Gong Queen Mary University of London
  • Xiatian Zhu Vision Semantics Limited




Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progressive affinity diffusion process. Extensive experiments on object image classification and clustering show the performance superiority of the proposed method over the state-of-the-art unsupervised learning models using six common image recognition benchmarks including MNIST, SVHN, STL10, CIFAR10, CIFAR100 and ImageNet.




How to Cite

Huang, J., Dong, Q., Gong, S., & Zhu, X. (2020). Unsupervised Deep Learning via Affinity Diffusion. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11029-11036. https://doi.org/10.1609/aaai.v34i07.6757



AAAI Technical Track: Vision