New Results on Sparse Autoencoders for Posture Classification and Segmentation
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN’20), pages 187--192 - Oct 2020.
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)", series = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning", pages = "187--192", month = "Oct", year = "2020", url = "https://www2.informatik.uni-hamburg.de/wtm/publications/2020/JW20/2020JirakWermterESANN.pdf" }