Computer vision & multimodal· 2014
Going Deeper with Convolutions
On the reading lists
- #25 in the Top 500 (2026 edition)
- #9 in Computer vision & multimodal (2026 edition)
Why it matters
Introduced computationally efficient multiscale convolutional modules, enabling deeper and wider networks.
Condensed from the expert lists' notes.
The record
- First released
- 2014 (from arXiv 1409.4842)
- Published
- 2015
- Citations
- 48,226 citations, as counted by Papers With Code (CC BY-SA 4.0)
- Awards
- CVPR Longuet-Higgins Prize
- Named by
- both expert lists
- Also relevant to
- Deep learning
- Score
- 0.757 · computed rank 25 · consensus 0.62, must-read 0.5, citations percentile 0.918 (2010s · vision), awards 1, existing lists 0.5. Method
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.