Historical milestone · 2016
Deep residual learning for image recognition
Reviewed through September 18, 2026
2016 · Historical milestone
Deep residual learning for image recognition
- Era
- 2010s
- Theme
- Machine-learning foundations
- Evidence form
- Architecture + benchmark experiment
- School / paradigm
- Connectionist / representation learning
- Institution / context
- Microsoft Research Asia
- Researchers
- Kaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Used identity shortcut connections to optimize substantially deeper convolutional networks.
Result / historical claim. A 152-layer residual network won the ILSVRC 2015 classification task and made depth easier to train.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Architecture + benchmark experiment
Limitation / debate. Evidence was architecture- and benchmark-specific and still depended on labeled data and compute.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
No primary-source URL is recorded for this entry in our reviewed data. Rather than manufacture a citation, we link the Implement Agentic research page that carries the record.
Reproduce
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A reproduction tutorial is not yet available for this entry. The closest reviewed material is .
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.