Unsupervised Neural Segmentation and Clustering for Unit Discovery in Sequential Data

Jan Chorowski 1 Nanxin Chen 2 Ricard Marxer 3 Hans Dolfing Adrian Łańcucki 1 Guillaume Sanchez 3 Tanel Alumäe 4 Antoine Laurent 5
1 University of Wrocław [Poland]
2 JHU - Johns Hopkins University
3 DYNI - DYNamiques de l’Information
LIS - Laboratoire d'Informatique et Systèmes
4 TTÜ - Tallinn University of Technology
5 LIUM - Laboratoire d'Informatique de l'Université du Mans
Abstract : We study the problem of unsupervised segmentation and clustering of handwritten lines with applications to character discovery. We propose a constrained variant of Vector Quantized Variational Autoencoder (VQ-VAE) which produces a discrete and piecewise-constant encoding of the data. We show that the constrained quantization task is dual to a Markovian dynamics prior placed on the latent codes. Such view facilitates a probabilistic interpretation of the constraints and allows efficient inference. We demonstrate the effectiveness of the proposed method in the context of unsupervised handwriting character discovery in 17th-century scanned manuscripts.
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Submitted on : Sunday, December 8, 2019 - 9:57:22 PM
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Jan Chorowski, Nanxin Chen, Ricard Marxer, Hans Dolfing, Adrian Łańcucki, et al.. Unsupervised Neural Segmentation and Clustering for Unit Discovery in Sequential Data. NeurIPS 2019 workshop - Perception as generative reasoning - Structure, Causality, Probability, Dec 2019, Vancouver, Canada. ⟨hal-02399138⟩

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