chore: import upstream snapshot with attribution

2026-07-13 12:19:01 +08:00
commit 3b90d1192f
2172 changed files with 594509 additions and 0 deletions
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+{
+ "<h1>Deep Convolutional Generative Adversarial Networks (DCGAN)</h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper <a href=\"https://arxiv.org/abs/1511.06434\">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>.</p>\n<p>This implementation is based on the <a href=\"https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\">PyTorch DCGAN Tutorial</a>.</p>\n": "<h1>\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u578b\u6575\u5bfe\u7684\u751f\u6210\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (DCGAN)</h1>\n<p>\u3053\u308c\u306f\u3001<a href=\"https://pytorch.org\"><a href=\"https://arxiv.org/abs/1511.06434\">\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u751f\u6210\u578b\u6575\u5bfe\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u6559\u5e2b\u306a\u3057\u8868\u73fe\u5b66\u7fd2\u306ePyTorch\u5b9f\u88c5\u3067\u3059</a></a>\u3002</p>\n<p>\u3053\u306e\u5b9f\u88c5\u306f <a href=\"https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\">PyTorch DCGAN</a> \u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306b\u57fa\u3065\u3044\u3066\u3044\u307e\u3059\u3002</p>\n",
+ "<h3>Convolutional Discriminator Network</h3>\n": "<h3>\u7573\u307f\u8fbc\u307f\u5f01\u5225\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</h3>\n",
+ "<h3>Convolutional Generator Network</h3>\n<p>This is similar to the de-convolutional network used for CelebA faces, but modified for MNIST images.</p>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>\u7573\u307f\u8fbc\u307f\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</h3>\n<p>\u3053\u308c\u306f CeleBA \u30d5\u30a7\u30fc\u30b9\u306b\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u30c7\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30ca\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u4f3c\u3066\u3044\u307e\u3059\u304c\u3001MNIST \u30a4\u30e1\u30fc\u30b8\u7528\u306b\u5909\u66f4\u3055\u308c\u3066\u3044\u307e\u3059\u3002</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p>Change from shape <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u5f62\u72b6\u3092\u6b21\u306e\u3088\u3046\u306b\u5909\u66f4 <span translate=no>_^_1_^_</span></p>\n",
+ "<p>The input is <span translate=no>_^_0_^_</span> with 100 channels </p>\n": "<p><span translate=no>_^_0_^_</span>\u5165\u529b\u306f100\u30c1\u30e3\u30f3\u30cd\u30eb</p>\n",
+ "<p>The input is <span translate=no>_^_0_^_</span> with one channel </p>\n": "<p><span translate=no>_^_0_^_</span>\u5165\u529b\u306f1\u30c1\u30e3\u30f3\u30cd\u30eb\u3067\u3059</p>\n",
+ "<p>This gives <span translate=no>_^_0_^_</span> </p>\n": "<p>\u3053\u308c\u306b\u3088\u308a <span translate=no>_^_0_^_</span></p>\n",
+ "<p>This gives <span translate=no>_^_0_^_</span> output </p>\n": "<p><span translate=no>_^_0_^_</span>\u3053\u308c\u306b\u3088\u308a\u51fa\u529b\u304c\u5f97\u3089\u308c\u307e\u3059</p>\n",
+ "<p>We import the <a href=\"../original/experiment.html\">simple gan experiment</a> and change the generator and discriminator networks </p>\n": "<p><a href=\"../original/experiment.html\">\u7c21\u5358\u306aGAN\u5b9f\u9a13\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066</a>\u3001\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u3068\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5909\u66f4\u3057\u307e\u3059</p>\n",
+ "A simple PyTorch implementation/tutorial of Deep Convolutional Generative Adversarial Networks (DCGAN).": "\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u751f\u6210\u578b\u6575\u5bfe\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08DCGAN\uff09\u306e\u7c21\u5358\u306aPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
+ "Deep Convolutional Generative Adversarial Networks (DCGAN)": "\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u578b\u6575\u5bfe\u7684\u751f\u6210\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (DCGAN)"
+}
@@ -0,0 +1,13 @@
+{
+ "<h1>Deep Convolutional Generative Adversarial Networks (DCGAN)</h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper <a href=\"https://arxiv.org/abs/1511.06434\">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>.</p>\n<p>This implementation is based on the <a href=\"https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\">PyTorch DCGAN Tutorial</a>.</p>\n": "<h1>\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4\u0dc3\u0d82\u0dc0\u0dc4\u0db1 \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd (DCGAN)</h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://pytorch.org\">PyTorch</a> <a href=\"https://arxiv.org/abs/1511.06434\">\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0d82\u0d9a\u0ddd\u0da0\u0db1 \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd\u0dba\u0db1\u0dca \u0dc3\u0db8\u0d9f \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0d85\u0db0\u0dd3\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0db1\u0ddc\u0d9a\u0dc5 \u0db1\u0dd2\u0dba\u0ddd\u0da2\u0db1 \u0d89\u0d9c\u0dd9\u0db1\u0dd4\u0db8\u0dca</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dba\u0dd2. </p>\n<p>\u0db8\u0dd9\u0db8\u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 <a href=\"https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\">PyTorch DCGAN \u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba</a>\u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca \u0dc0\u0dda. </p>\n",
+ "<h3>Convolutional Discriminator Network</h3>\n": "<h3>\u0dc3\u0d82\u0dc0\u0dd2\u0da0\u0dca\u0da1\u0dda\u0daf\u0d9a\u0dc0\u0dd2\u0dc3\u0d82\u0dc0\u0dcf\u0daf\u0dd3 \u0da2\u0dcf\u0dbd\u0dba</h3>\n",
+ "<h3>Convolutional Generator Network</h3>\n<p>This is similar to the de-convolutional network used for CelebA faces, but modified for MNIST images.</p>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>\u0dc3\u0d82\u0dc0\u0dbb\u0dca\u0dad\u0da2\u0dcf\u0dbd \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba</h3>\n<p>\u0db8\u0dd9\u0dba\u0dc3\u0dd9\u0dbd\u0dd9\u0db6\u0dcf \u0db8\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dc3\u0db3\u0dc4\u0dcf \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1 \u0daf-\u0dc3\u0d82\u0dc0\u0dc4\u0db1 \u0da2\u0dcf\u0dbd\u0dba\u0da7 \u0dc3\u0db8\u0dcf\u0db1 \u0dc0\u0db1 \u0db1\u0db8\u0dd4\u0dad\u0dca MNIST \u0dbb\u0dd6\u0db4 \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0d9a\u0dbb \u0d87\u0dad. </p>\n<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p>Change from shape <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca\u0dc0\u0dd9\u0db1\u0dc3\u0dca <span translate=no>_^_0_^_</span> \u0d9a\u0dbb\u0db1\u0dca\u0db1 <span translate=no>_^_1_^_</span> </p>\n",
+ "<p>The input is <span translate=no>_^_0_^_</span> with 100 channels </p>\n": "<p>\u0d86\u0daf\u0dcf\u0db1\u0dba\u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf 100 \u0d9a\u0dca <span translate=no>_^_0_^_</span> \u0dc3\u0db8\u0d9f \u0d87\u0dad </p>\n",
+ "<p>The input is <span translate=no>_^_0_^_</span> with one channel </p>\n": "<p>\u0d86\u0daf\u0dcf\u0db1\u0dba\u0d91\u0d9a\u0dca \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf\u0dc0\u0d9a\u0dca <span translate=no>_^_0_^_</span> \u0dc3\u0db8\u0d9f \u0d87\u0dad </p>\n",
+ "<p>This gives <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db8\u0dd9\u0dba\u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0dba\u0dd2 <span translate=no>_^_0_^_</span> </p>\n",
+ "<p>This gives <span translate=no>_^_0_^_</span> output </p>\n": "<p>\u0db8\u0dd9\u0dba <span translate=no>_^_0_^_</span> \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0dba\u0dd2 </p>\n",
+ "<p>We import the <a href=\"../original/experiment.html\">simple gan experiment</a> and change the generator and discriminator networks </p>\n": "<p>\u0d85\u0db4\u0dd2 <a href=\"../original/experiment.html\">\u0dc3\u0dbb\u0dbd \u0d9c\u0dd0\u0db1\u0dca \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8\u0dca</a> \u0d86\u0db1\u0dba\u0db1\u0dba \u0d9a\u0dbb \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba \u0dc3\u0dc4 \u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca \u0d9a\u0dbb\u0db1 \u0da2\u0dcf\u0dbd \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0d9a\u0dbb\u0db8\u0dd4 </p>\n",
+ "A simple PyTorch implementation/tutorial of Deep Convolutional Generative Adversarial Networks (DCGAN).": "\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0d82\u0dba\u0dd4\u0d9a\u0dca\u0dad \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd (DCGAN) \u0dc3\u0dbb\u0dbd PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
+ "Deep Convolutional Generative Adversarial Networks (DCGAN)": "\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0d82\u0dc0\u0dc4\u0db1 \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd (DCGAN)"
+}
@@ -0,0 +1,13 @@
+{
+ "<h1>Deep Convolutional Generative Adversarial Networks (DCGAN)</h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper <a href=\"https://arxiv.org/abs/1511.06434\">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>.</p>\n<p>This implementation is based on the <a href=\"https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\">PyTorch DCGAN Tutorial</a>.</p>\n": "<h1>\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc (DCGAN)</h1>\n<p>\u8fd9\u662f <a href=\"https://pytorch.org\">PyTorch</a> \u5b9e\u73b0\u7684\u8bba\u6587\u300a<a href=\"https://arxiv.org/abs/1511.06434\">\u4f7f\u7528\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u8fdb\u884c\u65e0\u76d1\u7763\u8868\u793a\u5b66\u4e60</a>\u300b\u3002</p>\n<p>\u6b64\u5b9e\u73b0\u57fa\u4e8e <a href=\"https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\">PyTorch DCGAN \u6559\u7a0b</a>\u3002</p>\n",
+ "<h3>Convolutional Discriminator Network</h3>\n": "<h3>\u5377\u79ef\u9274\u522b\u5668\u7f51\u7edc</h3>\n",
+ "<h3>Convolutional Generator Network</h3>\n<p>This is similar to the de-convolutional network used for CelebA faces, but modified for MNIST images.</p>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>\u5377\u79ef\u751f\u6210\u5668\u7f51\u7edc</h3>\n<p>\u8fd9\u7c7b\u4f3c\u4e8e\u7528\u4e8e CeleBA \u4eba\u8138\u7684\u53cd\u5377\u79ef\u7f51\u7edc\uff0c\u4f46\u9488\u5bf9 MNIST \u56fe\u50cf\u8fdb\u884c\u4e86\u4fee\u6539\u3002</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p>Change from shape <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u4ece\u5f62\u72b6\u6539<span translate=no>_^_0_^_</span>\u4e3a<span translate=no>_^_1_^_</span></p>\n",
+ "<p>The input is <span translate=no>_^_0_^_</span> with 100 channels </p>\n": "<p>\u8f93\u5165<span translate=no>_^_0_^_</span>\u6709 100 \u4e2a\u901a\u9053</p>\n",
+ "<p>The input is <span translate=no>_^_0_^_</span> with one channel </p>\n": "<p>\u8f93\u5165<span translate=no>_^_0_^_</span>\u4f7f\u7528\u4e00\u4e2a\u901a\u9053</p>\n",
+ "<p>This gives <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8fd9\u7ed9\u4e86<span translate=no>_^_0_^_</span></p>\n",
+ "<p>This gives <span translate=no>_^_0_^_</span> output </p>\n": "<p>\u8fd9\u7ed9\u51fa\u4e86<span translate=no>_^_0_^_</span>\u8f93\u51fa</p>\n",
+ "<p>We import the <a href=\"../original/experiment.html\">simple gan experiment</a> and change the generator and discriminator networks </p>\n": "<p>\u6211\u4eec\u5bfc\u5165\u4e86<a href=\"../original/experiment.html\">\u7b80\u5355\u7684 gan \u5b9e\u9a8c</a>\u5e76\u66f4\u6539\u4e86\u751f\u6210\u5668\u548c\u9274\u522b\u5668\u7f51\u7edc</p>\n",
+ "A simple PyTorch implementation/tutorial of Deep Convolutional Generative Adversarial Networks (DCGAN).": "\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08DCGAN\uff09\u7684\u7b80\u5355\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\u3002",
+ "Deep Convolutional Generative Adversarial Networks (DCGAN)": "\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc (DCGAN)"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/gan/dcgan/index.html\">Deep Convolutional Generative Adversarial Networks - DCGAN</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper <a href=\"https://arxiv.org/abs/1511.06434\">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>. </p>\n": "<h1><a href=\"https://nn.labml.ai/gan/dcgan/index.html\">\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u751f\u6210\u578b\u6575\u5bfe\u30cd\u30c3\u30c8\u30ef\u30fc\u30af-DCGAN</a></h1>\n<p>\u3053\u308c\u306f\u3001<a href=\"https://pytorch.org\"><a href=\"https://arxiv.org/abs/1511.06434\">\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u751f\u6210\u578b\u6575\u5bfe\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u6559\u5e2b\u306a\u3057\u8868\u73fe\u5b66\u7fd2\u306ePyTorch\u5b9f\u88c5\u3067\u3059</a></a>\u3002</p>\n",
+ "Deep Convolutional Generative Adversarial Networks - DCGAN": "\u6df1\u5c64\u7573\u307f\u8fbc\u307f\u751f\u6210\u578b\u6575\u5bfe\u30cd\u30c3\u30c8\u30ef\u30fc\u30af-DCGAN"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/gan/dcgan/index.html\">Deep Convolutional Generative Adversarial Networks - DCGAN</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper <a href=\"https://arxiv.org/abs/1511.06434\">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>. </p>\n": "<h1><a href=\"https://nn.labml.ai/gan/dcgan/index.html\">\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0d82\u0dc0\u0dbb\u0dca\u0dad \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd - DCGAN</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://pytorch.org\">PyTorch</a> <a href=\"https://arxiv.org/abs/1511.06434\">\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0d82\u0d9a\u0ddd\u0da0\u0db1 \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd\u0dba\u0db1\u0dca \u0dc3\u0db8\u0d9f \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0d85\u0db0\u0dd3\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0db1\u0ddc\u0d9a\u0dc5 \u0db1\u0dd2\u0dba\u0ddd\u0da2\u0db1 \u0d89\u0d9c\u0dd9\u0db1\u0dd4\u0db8\u0dca</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dba\u0dd2. </p>\n",
+ "Deep Convolutional Generative Adversarial Networks - DCGAN": "\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0d82\u0dc0\u0dbb\u0dca\u0dad \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd - DCGAN"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/gan/dcgan/index.html\">Deep Convolutional Generative Adversarial Networks - DCGAN</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper <a href=\"https://arxiv.org/abs/1511.06434\">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>. </p>\n": "<h1><a href=\"https://nn.labml.ai/gan/dcgan/index.html\">\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc-DCGAN</a></h1>\n<p>\u8fd9\u662f <a href=\"https://pytorch.org\">PyTorch</a> \u5b9e\u73b0\u7684\u8bba\u6587\u300a<a href=\"https://arxiv.org/abs/1511.06434\">\u4f7f\u7528\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u8fdb\u884c\u65e0\u76d1\u7763\u8868\u793a\u5b66\u4e60</a>\u300b\u3002</p>\n",
+ "Deep Convolutional Generative Adversarial Networks - DCGAN": "\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc-DCGAN"
+}