Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification

Zoltan A. Milacski, Barnabas Poczos, Andras Lorincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we propose Group k-Sparse Temporal Convolutional Neural Networks for unsupervised pretraining using video data. Our work is the first to consider the recurrent extension of structured sparsity, thus enhancing representational power and explainability. We show that our architecture is able to outperform several state-of-the-art baselines on Rotated MNIST, Scanned CIFAR-10, COIL-100 and NEC Animal pretraining benchmarks for video classification using limited labeled data.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period7/14/197/19/19

    Fingerprint

Keywords

  • convolutional neural networks
  • group sparsity
  • temporal
  • unsupervised learning
  • video data

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Milacski, Z. A., Poczos, B., & Lorincz, A. (2019). Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852057] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852057