readme.ja.json

{
 "<h1><a href=\"https://nn.labml.ai/diffusion/ddpm/index.html\">Denoising Diffusion Probabilistic Models (DDPM)</a></h1>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb\"><span translate=no>_^_0_^_</span></a></p>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation/tutorial of the paper <a href=\"https://arxiv.org/abs/2006.11239\">Denoising Diffusion Probabilistic Models</a>.</p>\n<p>In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images.</p>\n<p>Here is the <a href=\"https://nn.labml.ai/diffusion/ddpm/unet.html\">UNet model</a> that predicts the noise and <a href=\"https://nn.labml.ai/diffusion/ddpm/experiment.html\">training code</a>. <a href=\"https://nn.labml.ai/diffusion/ddpm/evaluate.html\">This file</a> can generate samples and interpolations from a trained model. </p>\n": "<h1><a href=\"https://nn.labml.ai/diffusion/ddpm/index.html\">\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb (DDPM)</a></h1>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb\"><span translate=no>_^_0_^_</span></a></p>\n<p><a href=\"https://arxiv.org/abs/2006.11239\">\u3053\u308c\u306f\u3001\u8ad6\u6587\u300c\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb\u300d<a href=\"https://pytorch.org\">\u306ePyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u3059</a>\u3002</a></p>\n<p>\u7c21\u5358\u306b\u8a00\u3046\u3068\u3001\u30c7\u30fc\u30bf\u304b\u3089\u753b\u50cf\u3092\u53d6\u5f97\u3057\u3001\u6bb5\u968e\u7684\u306b\u30ce\u30a4\u30ba\u3092\u8ffd\u52a0\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u3066\u5404\u30b9\u30c6\u30c3\u30d7\u3067\u305d\u306e\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c\u3057\u3001\u305d\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u753b\u50cf\u3092\u751f\u6210\u3057\u307e\u3059\u3002</p>\n<p><a href=\"https://nn.labml.ai/diffusion/ddpm/unet.html\"><a href=\"https://nn.labml.ai/diffusion/ddpm/experiment.html\">\u30ce\u30a4\u30ba\u3068\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b3\u30fc\u30c9\u3092\u4e88\u6e2c\u3059\u308b</a> uNet \u30e2\u30c7\u30eb\u3092\u6b21\u306b\u793a\u3057\u307e\u3059</a>\u3002<a href=\"https://nn.labml.ai/diffusion/ddpm/evaluate.html\">\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u3067\u306f</a>\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307f\u306e\u30e2\u30c7\u30eb\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3068\u88dc\u9593\u3092\u751f\u6210\u3067\u304d\u307e\u3059</p>\u3002\n",
 "Denoising Diffusion Probabilistic Models (DDPM)": "\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb (DDPM)"
}