# Supervised Encoding for Discrete Representation Learning

arXiv: https://arxiv.org/abs/1910.11067 · HTML: https://mlsanity.com/paper/1910.11067/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Transfer Learning for Individual Treatment Effect Estimation](https://mlsanity.com/paper/2210.00380/) (2022)
- [Improving Open-Domain Dialogue Evaluation with a Causal Inference Model](https://mlsanity.com/paper/2301.13372/) (2023)
- [Mode-Aware Continual Learning for Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2305.11400/) (2023)

## Abstract (arXiv)

Classical supervised classification tasks search for a nonlinear mapping that maps each encoded feature directly to a probability mass over the labels. Such a learning framework typically lacks the intuition that encoded features from the same class tend to be similar and thus has little interpretability for the learned features. In this paper, we propose a novel supervised learning model named Supervised-Encoding Quantizer (SEQ). The SEQ applies a quantizer to cluster and classify the encoded features. We found that the quantizer provides an interpretable graph where each cluster in the graph represents a class of data samples that have a particular style. We also trained a decoder that can decode convex combinations of the encoded features from similar and different clusters and provide guidance on style transfer between sub-classes.