ResNet: residual learning enables very deep networks
Confirmed
Importance: major (4 of 5)The takeaway
Kaiming He and colleagues introduced residual connections, allowing networks with 152+ layers to train; ResNet won ILSVRC-2015 with 3.57% top-5 error.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- Major (4 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- arXiv 1512.03385 (December 2015); CVPR 2016 best paper
- ILSVRC-2015 classification winner, 3.57% top-5 error
- Skip/residual connections are used in virtually all modern architectures, including Transformers
- Among the most-cited papers in all of science
What happened
Residual blocks learn a correction to an identity mapping, making optimization of very deep networks tractable.
Why it matters
Residual connections are a universal ingredient of deep learning; every Transformer block uses them.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 1 primary, 1 reaction
Primary
- Deep Residual Learning for Image Recognition (arXiv)arxiv.org, paper
Reactions
- Wikipedia: Residual neural networken.wikipedia.org, discussion
Changes
- Filed