# Deep Generative Neural Embeddings for High Dimensional Data Visualization

Source: [Deep Generative Neural Embeddings for High Dimensional Data Visualization](<https://www.sota2.com/research/paper/68fa6e9a1e209131cea41073>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:01:51.507Z

Authors: Halid Ziya Yerebakan, Gerardo Hermosillo Valadez

Published: 2023-01-25T14:18:09.000Z

## Abstract

We propose a visualization technique that utilizes neural network embeddings and a generative network to reconstruct original data. This method allows for independent manipulation of individual image embeddings through its non-parametric structure, providing more flexibility than traditional autoencoder approaches. We have evaluated the effectiveness of this technique in data visualization and compared it to t-SNE and VAE methods. Furthermore, we have demonstrated the scalability of our method through visualizations on the ImageNet dataset. Our technique has potential applications in human-in-the-loop training, as it allows for independent editing of embedding locations without affecting the optimization process.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2302.10801>)

[PDF](<https://arxiv.org/pdf/2302.10801.pdf>)