mlsanityData to 7 Oct 2026

Variational Autoencoder for Deep Learning of Images, Labels and Captions

arXiv 1609.08976 ↗First posted 28 Sep 2016stat.ML cs.LGcited by 137

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Abstract (arXiv)

A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of the latent image features, and a deep Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used to approximate a distribution for the latent DGDN features/code. The latent code is also linked to generative models for labels (Bayesian support vector machine) or captions (recurrent neural network). When predicting a label/caption for a new image at test, averaging is performed across the distribution of latent codes; this is computationally efficient as a consequence of the learned CNN-based encoder. Since the framework is capable of modeling the image in the presence/absence of associated labels/captions, a new semi-supervised setting is manifested for CNN learning with images; the framework even allows unsupervised CNN learning, based on images alone.

Key facts. Cited by 137 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 29; 2022: 29; 2023: 22; 2024: 19; 2025: 24; 2026: 13.

137 papers.

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