# Comment on "Ensemble Projection for Semi-supervised Image Classification"

Source: [Comment on "Ensemble Projection for Semi-supervised Image Classification"](<https://www.sota2.com/research/paper/68fa617b1e209131ce9f45be>)

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

Data updated: 2025-10-25T19:59:37.091Z

Authors: Xavier Boix, Gemma Roig, Luc Van Gool

Published: 2014-08-29T09:37:56.000Z

## Abstract

In a series of papers by Dai and colleagues \[1,2\], a feature map (or kernel) was introduced for semi- and unsupervised learning. This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels. In this critique, we analyze the latest version of this series of papers, which is called Ensemble Projections \[2\]. We show that the results reported in \[2\] were not well conducted, and that Ensemble Projections performs poorly for semi-supervised learning.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1408.6963>)

[PDF](<https://arxiv.org/pdf/1408.6963.pdf>)