Semi-Supervised Learning via Compact Latent Space Clustering
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2018
- DOI:
- 10.48550/arXiv.1806.02679
- arXiv:
- arXiv:1806.02679
- Bibcode:
- 2018arXiv180602679K
- Keywords:
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- Computer Science - Machine Learning;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Neural and Evolutionary Computing;
- Statistics - Machine Learning
- E-Print:
- Presented as a long oral in ICML 2018. Post-conference camera ready