A Deeper Look at Dataset Bias

TL;DR

This paper investigates the effectiveness of DeCAF features in addressing dataset bias in computer vision, analyzing dataset differences and evaluating debiasing methods to identify remaining challenges.

Contribution

It introduces the use of DeCAF features for dataset bias analysis and evaluates existing debiasing techniques across different representations.

Findings

DeCAF features show promise in mitigating dataset bias.

Some debiasing methods perform well with DeCAF features.

Open questions remain in fully solving dataset bias issues.

Abstract

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid development of deep learning architectures, the activation values of Convolutional Neural Networks (CNN) are emerging as reliable and robust image descriptors. In this paper we propose to verify the potential of the DeCAF features when facing the dataset bias problem. We conduct a series of analyses looking at how existing datasets differ among each other and verifying the performance of existing debiasing methods under different representations. We learn important lessons on which part of the dataset bias problem can be considered solved and which open questions still need to be tackled.