Self-labelling via simultaneous clustering and representation learning

Conference item

Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm. The resulting method is able to self-label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet and yields the first self-supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available.

Authors: Vedaldi, A; Asano, Y; Rupprecht, C

• Publication status: Published
• Peer review status: Under review
• Publisher: Open Review
• Pages: 1-22
• Publication date: 2020-03-11
• Acceptance date: 2019-12-19
• Event title: International Conference on Learning Representations 2020 (ICLR 2020)
• Event location: Addis Ababa, Ethiopia
• Event website: https://iclr.cc/Conferences/2020
• Event start date: 2020-04-30
• Event end date: 2020-04-30
• Language: English
• Keywords: FFR; clustering; self-supervision; feature representation learning