@inproceedings{kamnitsas2018semi-supervised,
author = {Kamnitsas, Konstantinos and C. Castro, Daniel and Le Folgoc, Loic and Walker, Ian and Tanno, Ryutaro and Rueckert, Daniel and Glocker, Ben and Criminisi, Antonio and Nori, Aditya},
title = {Semi-Supervised Learning via Compact Latent Space Clustering},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2018},
month = {June},
abstract = {We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.},
url = {http://approjects.co.za/?big=en-us/research/publication/semi-supervised-learning-via-compact-latent-space-clustering/},
}