DMCA
Processing Images By Semi-Linear Predictability Minimization (1999)
| Venue: | Network: Computation in Neural Systems |
| Citations: | 6 - 4 self |
BibTeX
@INPROCEEDINGS{Schraudolph99processingimages,
author = {Nicol Schraudolph and Martin Eldracher and Jürgen Schmidhuber},
title = {Processing Images By Semi-Linear Predictability Minimization},
booktitle = {Network: Computation in Neural Systems},
year = {1999},
pages = {133--169},
publisher = {Springer Verlag}
}
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Abstract
In the predictability minimization approach (Schmidhuber 1992), input patterns are fed into a system consisting of adaptive, initially unstructured feature detectors. There are also adaptive predictors constantly trying to predict current feature detector outputs from other feature detector outputs. Simultaneously, however, the feature detectors try to become as unpredictable as possible, resulting in a co-evolution of predictors and feature detectors. This paper describes the implementation of a visual processing system trained by semi-linear predictability minimization, and presents many experiments that examine its response to artificial and real-world images. In particular, we observe that under a wide variety of conditions, predictability minimization results in the development of well-known visual feature detectors.
Keyphrases
semi-linear predictability minimization feature detector feature detector output predictability minimization approach input pattern present many experiment visual processing system current feature detector output real-world image wide variety predictability minimization result well-known visual feature detector adaptive predictor unstructured feature detector
