Learning to Linearize Under Uncertainty
Abstract
Training deep feature hierarchies to solve supervised learning tasks has achieving state of the art performance on many problems in computer vision. However, a principled way in which to train such hierarchies in the unsupervised setting has remained elusive. In this work we suggest a new architecture and loss for training deep feature hierarchies that linearize the transformations observed in unlabelednatural video sequences. This is done by training a generative model to predict video frames. We also address the problem of inherent uncertainty in prediction by introducing a latent variables that are non-deterministic functions of the input into the network architecture.
Cite
Text
Goroshin et al. "Learning to Linearize Under Uncertainty." Neural Information Processing Systems, 2015.Markdown
[Goroshin et al. "Learning to Linearize Under Uncertainty." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/goroshin2015neurips-learning/)BibTeX
@inproceedings{goroshin2015neurips-learning,
title = {{Learning to Linearize Under Uncertainty}},
author = {Goroshin, Ross and Mathieu, Michael F and LeCun, Yann},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {1234-1242},
url = {https://mlanthology.org/neurips/2015/goroshin2015neurips-learning/}
}