Research map / Probabilistic and scientific modeling / Variational and generative sampling
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Disentangling Disentanglement in Variational Autoencoders (ICML 2019 · similarity 0.93)
- Variational Autoencoders Pursue PCA Directions (by Accident) (CVPR 2019 · similarity 0.92)
- Consistency Regularization for Variational Auto-Encoders (NeurIPS 2021 · similarity 0.91)
- Guided Variational Autoencoder for Disentanglement Learning (CVPR 2020 · similarity 0.90)
- An Identifiable Double VAE For Disentangled Representations (ICML 2021 · similarity 0.90)
- Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations (ICML 2023 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
