# A Testbed for Cross-Dataset Analysis

arXiv: https://arxiv.org/abs/1402.5923 · HTML: https://mlsanity.com/paper/1402.5923/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2014.

## Built on by (most cited first, 5 of 5)

- [Editing a classifier by rewriting its prediction rules](https://mlsanity.com/paper/2112.01008/) (2021)
- [Deep transfer learning for image classification: a survey](https://mlsanity.com/paper/2205.09904/) (2022)
- [Efficient Human-in-the-loop System for Guiding DNNs Attention](https://mlsanity.com/paper/2206.05981/) (2022)
- [Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection](https://mlsanity.com/paper/2409.17158/) (2024)
- [Exploring Scale Shift in Crowd Localization under the Context of Domain Generalization](https://mlsanity.com/paper/2510.19330/) (2025)

## Abstract (arXiv)

Since its beginning visual recognition research has tried to capture the huge variability of the visual world in several image collections. The number of available datasets is still progressively growing together with the amount of samples per object category. However, this trend does not correspond directly to an increasing in the generalization capabilities of the developed recognition systems. Each collection tends to have its specific characteristics and to cover just some aspects of the visual world: these biases often narrow the effect of the methods defined and tested separately over each image set. Our work makes a first step towards the analysis of the dataset bias problem on a large scale. We organize twelve existing databases in a unique corpus and we present the visual community with a useful feature repository for future research.