论文标题
使用三重态增强自动编码器的矢量嵌入具有子向量置换不变性的嵌入
Vector Embeddings with Subvector Permutation Invariance using a Triplet Enhanced Autoencoder
论文作者
论文摘要
由于其广泛的适用性，使用深神经网络（DNN）自动编码器（AES）最近爆炸了。但是，由标准DNN AE产生的嵌入表示形式，该表示仅训练以最小化重建误差，并不总是揭示数据中更微妙的模式。有时，自动编码器需要以一个或多个附加损失功能的形式进一步的方向。在本文中，我们使用增强的自动编码器随着三重损失的增强，以促进通过组成子向量排列相关的向量的聚类。通过这种方法，我们可以创建一个向量的嵌入，这几乎是此类排列不变的。然后，我们可以将这些不变的嵌入作为对其他问题的输入，例如分类和聚类，并提高这些问题的检测准确性。
The use of deep neural network (DNN) autoencoders (AEs) has recently exploded due to their wide applicability. However, the embedding representation produced by a standard DNN AE that is trained to minimize only the reconstruction error does not always reveal more subtle patterns in the data. Sometimes, the autoencoder needs further direction in the form of one or more additional loss functions. In this paper, we use an autoencoder enhanced with triplet loss to promote the clustering of vectors that are related through permutations of constituent subvectors. With this approach, we can create an embedding of the vector that is nearly invariant to such permutations. We can then use these invariant embeddings as inputs to other problems, like classification and clustering, and improve detection accuracy in those problems.
