Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction

6 May 2020Toshiaki Koike-AkinoYe Wang

We propose a new concept of rateless auto-encoders (RL-AEs) that enable a flexible latent dimensionality, which can be seamlessly adjusted for varying distortion and dimensionality requirements. In the proposed RL-AEs, instead of a deterministic bottleneck architecture, we use an over-complete representation that is stochastically regularized with weighted dropouts, in a manner analogous to sparse AE (SAE)... (read more)

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