论文标题
隐式排名最小化自动编码器
Implicit Rank-Minimizing Autoencoder
论文作者
论文摘要
自动编码器的重要组成部分是该方法的潜在表示信息能力最小化或有限。在这项工作中，代码的协方差矩阵的排名通过依赖于多层线性网络中的梯度下降学习导致最小级别的解决方案，从而隐式地最小化了。通过在编码器和解码器之间插入多个额外的线性层，系统自发地学习具有低有效尺寸的表示形式。该模型被称为隐式级别最小化自动编码器（IRMAE）是简单，确定性的，并且可以学习紧凑的潜在空间。我们演示了该方法对几个图像生成和表示学习任务的有效性。
An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly minimized by relying on the fact that gradient descent learning in multi-layer linear networks leads to minimum-rank solutions. By inserting a number of extra linear layers between the encoder and the decoder, the system spontaneously learns representations with a low effective dimension. The model, dubbed Implicit Rank-Minimizing Autoencoder (IRMAE), is simple, deterministic, and learns compact latent spaces. We demonstrate the validity of the method on several image generation and representation learning tasks.
