{
  "id": "diffusion-input-perturbation",
  "title": "Input Perturbation Reduces Exposure Bias in Diffusion Models",
  "authors": [
    {
      "given": "Mang",
      "family": "Ning"
    },
    {
      "given": "Enver",
      "family": "Sangineto"
    },
    {
      "given": "Angelo",
      "family": "Porrello"
    },
    {
      "given": "Simone",
      "family": "Calderara"
    },
    {
      "given": "Rita",
      "family": "Cucchiara"
    }
  ],
  "year": 2023,
  "venue": "ICML 2023",
  "paper_url": "https://proceedings.mlr.press/v202/ning23a.html",
  "pdf_url": "https://proceedings.mlr.press/v202/ning23a/ning23a.pdf",
  "summary": "研究扩散模型采样过程中累积的错误，并在训练中扰动输入，以模拟推理时的预测误差。",
  "connection": "通过合成输入扰动处理同一种训练与采样不匹配问题；Scheduled Sampling 则在模型生成的状态上训练。",
  "bibtex_type": "inproceedings",
  "citation_key": "pmlr-v202-ning23a",
  "source_urls": [
    "https://proceedings.mlr.press/v202/ning23a.html"
  ],
  "updated_at": "2026-11-11",
  "description_language": "zh-Hans",
  "abstract_language": "en",
  "record_url": "https://yuntiandeng.com/research/zh/papers/diffusion-input-perturbation/"
}