New Results on Sparse Autoencoders for Posture Classification and Segmentation

Doreen Jirak , Stefan Wermter

Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN’20), pages 187--192, - Oct 2020

Associated documents :

This paper is a sequel on posture recognition using sparse autoencoders. We conduct experiments on a posture dataset and show that shallow sparse autoencoders achieve even better performance compared to a convolutional neural network, state-of-the-art model for recognition tasks. Also, our results support robust image representation from the autoencoder model rendering further finetuning unnecessary. Finally, we suggest using sparse autoencoders for image segmentation.

@InProceedings{JW20,
 	 author =  {Jirak, Doreen and Wermter, Stefan},
 	 title = {New Results on Sparse Autoencoders for Posture Classification and Segmentation},
 	 booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN’20)},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {187--192},
 	 year = {2020},
 	 month = {Oct},
 	 publisher = {None},
 	 doi = {}
 }