LatentGAN自编码器：学习解耦的潜在分布
LatentGAN Autoencoder: Learning Disentangled Latent Distribution
AI总结：
提出LatentGAN自编码器，用生成器学习潜在分布并加信息论约束，在MNIST等数据集上实现解耦控制，无监督分类错误率2.38，优于InfoGAN和AAE。
摘要：
在自编码器中，编码器通常逼近数据集上的潜在分布，解码器则利用该学习到的潜在分布生成样本。由于使用随机潜在向量进行生成会导致平凡的输出，因此对潜在向量的控制非常有限。本工作试图通过使用LatentGAN生成器直接学习逼近自编码器的潜在分布来解决这一问题，并在MNIST、3D Chair和CelebA数据集上展示了有意义的结果，同时引入了一个额外的信息论约束，成功实现了对自编码器潜在分布的控制。由此，我们的模型在MNIST无监督图像分类上达到了2.38的错误率，优于InfoGAN和AAE。
英文摘要：
In autoencoder, the encoder generally approximates the latent distribution over the dataset, and the decoder generates samples using this learned latent distribution. There is very little control over the latent vector as using the random latent vector for generation will lead to trivial outputs. This work tries to address this issue by using the LatentGAN generator to directly learn to approximate the latent distribution of the autoencoder and show meaningful results on MNIST, 3D Chair, and CelebA datasets, an additional information-theoretic constrain is used which successfully learns to control autoencoder latent distribution. With this, our model also achieves an error rate of 2.38 on MNIST unsupervised image classification, which is better as compared to InfoGAN and AAE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
