In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised to quickly replace engineered representations, such as SIFT and HOG. However, compared to SIFT and HOG, we understand much less about the nature of the features learned by large CNNs. In this paper, we experimentally probe several aspects of CNN feature learning in an attempt to help practitioners gain useful, evidence-backed intuitions about how to apply CNNs to computer vision problems. © 2014 Springer International Publishing.
CITATION STYLE
Agrawal, P., Girshick, R., & Malik, J. (2014). Analyzing the performance of multilayer neural networks for object recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8695 LNCS, pp. 329–344). Springer Verlag. https://doi.org/10.1007/978-3-319-10584-0_22
Mendeley helps you to discover research relevant for your work.