Pith Number

pith:PCFYDLTO

pith:2015:PCFYDLTODMVFSOODEHBMBTOTEX

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Deep Residual Learning for Image Recognition

Jian Sun, Kaiming He, Shaoqing Ren, Xiangyu Zhang

Residual networks reformulate layers to learn differences from inputs via identity shortcuts, making much deeper training feasible and more accurate.

arxiv:1512.03385 v1 · 2015-12-10 · cs.CV

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

C2weakest assumption

That the residual functions with identity shortcuts are substantially easier to optimize than the original unreferenced mappings, which the paper supports through experiments but does not prove theoretically.

C3one line summary

Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.

References

52 extracted · 52 resolved · 1 Pith anchors

[1] Y . Bengio, P. Simard, and P. Frasconi. Learning long-term dependen- cies with gradient descent is difﬁcult. IEEE Transactions on Neural Networks, 5(2):157–166, 1994 1994

[2] C. M. Bishop. Neural networks for pattern recognition . Oxford university press, 1995 1995

[3] W. L. Briggs, S. F. McCormick, et al. A Multigrid Tutorial. Siam, 2000 2000

[4] K. Chatﬁeld, V . Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, 2011 2011

[5] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zis- serman. The Pascal Visual Object Classes (VOC) Challenge. IJCV, pages 303–338, 2010 2010

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Receipt and verification

First computed	2026-07-04T20:43:20.475735Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

788b81ae6e1b2a5939c321c2c0cdd325f377d0fb5a3193bb81fa6d3c44768a05

Aliases

arxiv: 1512.03385 · arxiv_version: 1512.03385v1 · doi: 10.48550/arxiv.1512.03385 · pith_short_12: PCFYDLTODMVF · pith_short_16: PCFYDLTODMVFSOOD · pith_short_8: PCFYDLTO

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PCFYDLTODMVFSOODEHBMBTOTEX \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 788b81ae6e1b2a5939c321c2c0cdd325f377d0fb5a3193bb81fa6d3c44768a05

Canonical record JSON

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    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2015-12-10T19:51:55Z",
    "title_canon_sha256": "697404a43beb20f7fff7d7cad129017d947ddc3f622768c25861d5bcd9ae9177"
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