论文标题
自动编码器图像插值通过塑造潜在空间
Autoencoder Image Interpolation by Shaping the Latent Space
论文作者
论文摘要
自动编码器代表了计算表征不同类型数据集的基本因素的有效方法。已经研究了在数据点之间通过解码潜在向量的凸组合来实现数据点之间的插值的潜在表示。然而，这种插值通常会导致伪影或在重建过程中产生不切实际的结果。我们认为这些不一致性是由于潜在空间的结构造成的，并且因为如此天真的插值潜在向量偏离了数据歧管。在本文中，我们提出了一种正则化技术，该技术塑造了潜在表示遵循与训练图像一致的歧管，并将歧管驱动为平滑且局部凸。这种正则化不仅可以使数据点之间的忠实插值，而且正如我们本文所示，还可以用作一般正规化技术，以避免过度拟合或生成新的样品以增加数据。
Autoencoders represent an effective approach for computing the underlying factors characterizing datasets of different types. The latent representation of autoencoders have been studied in the context of enabling interpolation between data points by decoding convex combinations of latent vectors. This interpolation, however, often leads to artifacts or produces unrealistic results during reconstruction. We argue that these incongruities are due to the structure of the latent space and because such naively interpolated latent vectors deviate from the data manifold. In this paper, we propose a regularization technique that shapes the latent representation to follow a manifold that is consistent with the training images and that drives the manifold to be smooth and locally convex. This regularization not only enables faithful interpolation between data points, as we show herein, but can also be used as a general regularization technique to avoid overfitting or to produce new samples for data augmentation.
