Pith Number

pith:IZHUHFCY

pith:2019:IZHUHFCYHPHS2S6LMXZ53T65C7

not attested not anchored not stored refs resolved

Generative Modeling by Estimating Gradients of the Data Distribution

Stefano Ermon, Yang Song

A generative model learns gradients of noisy data distributions to drive annealed Langevin dynamics and produce samples without adversarial training.

arxiv:1907.05600 v3 · 2019-07-12 · cs.LG · stat.ML

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Claims

C1strongest claim

Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments.

C2weakest assumption

Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores.

C3one line summary

Score-based generative modeling via multi-noise-level score matching and annealed Langevin dynamics produces samples on par with GANs and sets a new inception score record on CIFAR-10.

References

66 extracted · 66 resolved · 10 Pith anchors

[1] G. Alain, Y . Bengio, L. Yao, J. Yosinski, E. Thibodeau-Laufer, S. Zhang, and P. Vincent. GSNs: generative stochastic networks. Information and Inference, 2016 2016

[2] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In D. Precup and Y . W. Teh, editors,Proceedings of the 34th International Conference on Ma- chine Learning, volum 2017

[3] M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data represen- tation. Neural computation, 15(6):1373–1396, 2003 2003

[4] Y . Bengio, L. Yao, G. Alain, and P. Vincent. Generalized denoising auto-encoders as generative models. In Advances in neural information processing systems, pages 899–907, 2013 2013

[5] Learning to Generate Samples from Noise through Infusion Training 2017 · arXiv:1703.06975

Formal links

2 machine-checked theorem links

Cited by

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A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios

Shap-E: Generating Conditional 3D Implicit Functions

Receipt and verification

First computed	2026-05-17T23:38:13.893886Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

464f4394583bcf2d4bcb65f3ddcfdd17f17ca9e6a108204ca48924543df5f7e3

Aliases

arxiv: 1907.05600 · arxiv_version: 1907.05600v3 · doi: 10.48550/arxiv.1907.05600 · pith_short_12: IZHUHFCYHPHS · pith_short_16: IZHUHFCYHPHS2S6L · pith_short_8: IZHUHFCY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IZHUHFCYHPHS2S6LMXZ53T65C7 \
  | 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: 464f4394583bcf2d4bcb65f3ddcfdd17f17ca9e6a108204ca48924543df5f7e3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "8b8a8ca576822bf40b200dc900d7ab59460fc585198d16880952c92d2eac2423",
    "cross_cats_sorted": [
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2019-07-12T07:37:26Z",
    "title_canon_sha256": "b75f07f5b1033e09b1209c28be091524212e58474763bb135b7ec8620e416b82"
  },
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  "source": {
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    "kind": "arxiv",
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}