# A Plug-in Method for Representation Factorization in Connectionist Models

## TL;DR

This paper introduces FDEN, a plug-in network that decomposes latent representations in generative models into independent, interpretable factors, enabling semantic control without retraining the original models.

## Contribution

The paper presents a novel, unsupervised method for factorizing latent representations in existing models using FDEN, enhancing interpretability and controllability in generative tasks.

## Key findings

- Effective decomposition of latent factors demonstrated in image translation tasks.
- Improved semantic control in image generation without retraining models.
- Validated through extensive qualitative and quantitative experiments.

## Abstract

In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the original trained models. Particularly, we propose factors' decomposer-entangler network (FDEN) that learns to decompose a latent representation into mutually independent factors. Given a latent representation, the proposed framework draws a set of interpretable factors, each aligned to independent factors of variations by minimizing their total correlation in an information-theoretic means. As a plug-in method, we have applied our proposed FDEN to the existing networks of adversarially learned inference and pioneer network and performed computer vision tasks of image-to-image translation in semantic ways, e.g., changing styles, while keeping the identity of a subject, and object classification in a few-shot learning scheme. We have also validated the effectiveness of the proposed method with various ablation studies in the qualitative, quantitative, and statistical examination.