# Committees of deep feedforward networks trained with few data

Source: [Committees of deep feedforward networks trained with few data](<https://www.sota2.com/research/paper/68fa61681e209131ce9f42db>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T02:42:36.018Z

Authors: Bogdan Miclut, Thomas Kaester, Thomas Martinetz, Erhardt Barth

Published: 2014-06-23T15:34:54.000Z

## Abstract

Deep convolutional neural networks are known to give good results on image classification tasks. In this paper we present a method to improve the classification result by combining multiple such networks in a committee. We adopt the STL-10 dataset which has very few training examples and show that our method can achieve results that are better than the state of the art. The networks are trained layer-wise and no backpropagation is used. We also explore the effects of dataset augmentation by mirroring, rotation, and scaling.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on STL-10 (test)](<https://www.sota2.com/research/sota/image-classification-on-stl-10-test.md>): Image Classification; STL-10 (test); Accuracy: 68.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1406.5947>)

[PDF](<https://arxiv.org/pdf/1406.5947.pdf>)