# Classification by Re-generation: Towards Classification Based on Variational Inference

arXiv: https://arxiv.org/abs/1809.03259 · HTML: https://mlsanity.com/paper/1809.03259/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Adversarially Robust Classification by Conditional Generative Model Inversion](https://mlsanity.com/paper/2201.04733/) (2022)

## Abstract (arXiv)

As Deep Neural Networks (DNNs) are considered the state-of-the-art in many classification tasks, the question of their semantic generalizations has been raised. To address semantic interpretability of learned features, we introduce a novel idea of classification by re-generation based on variational autoencoder (VAE) in which a separate encoder-decoder pair of VAE is trained for each class. Moreover, the proposed architecture overcomes the scalability issue in current DNN networks as there is no need to re-train the whole network with the addition of new classes and it can be done for each class separately. We also introduce a criterion based on Kullback-Leibler divergence to reject doubtful examples. This rejection criterion should improve the trust in the obtained results and can be further exploited to reject adversarial examples.