Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels
The article discusses a new diagnostic framework for variational autoencoders (VAEs) that addresses the issue of mismatched decoding in neural codebook channels. It introduces a coupled encoder-decoder diagnostic that provides insights into the operational effectiveness of the latent space. The framework aims to enhance the understanding of how well the decoder interprets the encoder's code, which is crucial for improving deep generative models.
- ▪Classical communication systems can fail due to incompatible operational codebooks.
- ▪The proposed diagnostic framework includes a neural codebook channel that audits encoder-decoder performance.
- ▪The framework has been tested on multiple datasets, demonstrating its effectiveness in identifying mismatched decoding.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18846 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Machine Learning arXiv:2605.18846 (cs) [Submitted on 13 May 2026] Title:Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels Authors:Yusuke Hayashi View a PDF of the paper titled Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels, by Yusuke Hayashi View PDF HTML (experimental) Abstract:Classical communication systems fail not only through random noise but also when transmitter and receiver use incompatible operational codebooks. Variational autoencoders (VAEs) train an encoder $q_\phi$ and decoder $p_\theta$ jointly, and practitioners treat the resulting latent space as a discrete code -- for clustering, conditional generation, and mechanistic interpretability.
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