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20192022
most citedQuantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding

4 citations · 7 across the 4 of their papers we have counts for

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cs.LG2019★ 3 cited

Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space

Keizo Kato, Jing Zhou, Tomotake Sasaki +1

To analyze high-dimensional and complex data in the real world, deep generative models, such as variational autoencoder (VAE) embed data in a low-dimensional space (latent space) a…