# CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing

arXiv: https://arxiv.org/abs/2603.06361 · HTML: https://mlsanity.com/paper/2603.06361/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop](https://mlsanity.com/paper/2010.02506/) (2020)
- [AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes](https://mlsanity.com/paper/2008.12987/) (2020)
- [SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data](https://mlsanity.com/paper/2303.10182/) (2023)
- [A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems](https://mlsanity.com/paper/2512.05971/) (2025)
- [Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints](https://mlsanity.com/paper/2303.08068/) (2023)
- [Physical Layer Authentication Based on Hierarchical Variational Auto-Encoder for Industrial Internet of Things](https://mlsanity.com/paper/2508.06794/) (2025)

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

- [Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing](https://mlsanity.com/paper/2605.16433/) (2026)

## Abstract (arXiv)

Accurate fault detection in high-dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This paper introduces CLAIRE, i.e., a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems. It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features. The learned representations are then fed into a downstream classifier to perform binary fault prediction. Experimental results on a high-dimensional dataset demonstrate that CLAIRE significantly outperforms conventional classifiers trained directly on raw features. Moreover, the framework incorporates a post hoc phase, using a game-theory-based interpretability technique, to analyze the latent space and identify the most informative input features contributing to fault predictions. The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection. The modular and interpretable nature of the proposed framework makes it highly adaptable, offering promising applications in other domains characterized by complex, high-dimensional data, such as healthcare, finance, and environmental monitoring.