Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 294

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Fast image scanning with deep max-pooling convolutional neural networks

Giusti, Alessandro, Cireşan, Dan C., Masci, Jonathan, Gambardella, Luca M., Schmidhuber, Jürgen·2013·2013 IEEE International Conference on Image Processing

Abstract

Deep Neural Networks now excel at image classification, detection and segmentation. When used to scan images by means of a sliding window, however, their high computational complexity can bring even the most powerful hardware to its knees. We show how dynamic programming can speedup the process by orders of magnitude, even when max-pooling layers are present.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/icip.2013.6738831

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 18 references

Cited by

See all 294

Metadata

Authors
Giusti, A., Cireşan, D. C., Masci, J., Gambardella, L. M., & Schmidhuber, J.
Venue
2013 IEEE International Conference on Image Processing
Publisher
IEEE
Published
2013-09
Pages
4034-4038
ISSN
1522-4880 (ISSN-L)
OpenAlex
W2092985495
Cited by
294
References
18
FWCI
12.59
Type
conference paper
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Fast image scanning with deep max-pooling convolutional neural networks. https://app.cochat.ai/papers/10.1109/icip.2013.6738831
The paper
Giusti, A., Cireşan, D. C., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2013). Fast image scanning with deep max-pooling convolutional neural networks. 2013 IEEE International Conference on Image Processing. https://doi.org/10.1109/icip.2013.6738831

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.