# Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT Source: [Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:09:28.486Z Authors: Philipp Fischer, Alexey Dosovitskiy, Thomas Brox Published: 2014-05-22T14:35:52.000Z ## Abstract Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching. This paper has been merged with arXiv:1406.6909 ## 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]() [PDF]()