| _version_ | 1866901864136572928 |
|---|---|
| author | Hoang, Nguyen-Duc |
| author_facet | Hoang, Nguyen-Duc |
| contents | <div> <div># Quantifying PTM-Detector Geometric Alignment for Reconstruction-Based ASD</div> <br> <div>This repository contains the source code necessary to reproduce the experimental results presented in the paper "Quantifying PTM-detector geometric alignment for reconstruction-based anomalous sound detection".</div> <br> <div>The project investigates the effectiveness of 12 diverse Pre-trained Models (PTMs) for reconstruction-based Anomalous Sound Detection (ASD) using Autoencoders (AEs). It quantifies **representational alignment** using **debiased Centered Kernel Alignment (CKA)** and defines the empirical upper bound via a **supervised Late Fusion** XGBoost meta-learner.</div> </div> <p># Project Directory Structure</p> <p>This section details the critical output directories generated by the execution pipeline, which store cached features, trained models, and all analytical results (AUC scores, CKA matrices, and final plots) necessary to reproduce the findings of the paper.</p> <p>The overall structure is defined in the configuration files (e.g., `extract_ast.yaml` and `extract_late_fusion.yaml`):</p> <p>```<br>.<br>├── dataset # MIMII Dataset (Input data structure)<br>├── cache # Stores PTM embeddings (HDF5 files)<br>├── model # Stores trained Autoencoders (.pth)<br>└── result # Stores final YAML/CSV results<br> └── results_fusion # Fusion summary and plots (CKA, Figures)<br>``</p> <p>#All code in code.zip .</p> <p>#All data is the result of running the code in cache.zip, model.zip, results.zip.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17489579 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Quantifying PTM-detector geometric alignment for reconstruction-based anomalous sound detection Hoang, Nguyen-Duc <div> <div># Quantifying PTM-Detector Geometric Alignment for Reconstruction-Based ASD</div> <br> <div>This repository contains the source code necessary to reproduce the experimental results presented in the paper "Quantifying PTM-detector geometric alignment for reconstruction-based anomalous sound detection".</div> <br> <div>The project investigates the effectiveness of 12 diverse Pre-trained Models (PTMs) for reconstruction-based Anomalous Sound Detection (ASD) using Autoencoders (AEs). It quantifies **representational alignment** using **debiased Centered Kernel Alignment (CKA)** and defines the empirical upper bound via a **supervised Late Fusion** XGBoost meta-learner.</div> </div> <p># Project Directory Structure</p> <p>This section details the critical output directories generated by the execution pipeline, which store cached features, trained models, and all analytical results (AUC scores, CKA matrices, and final plots) necessary to reproduce the findings of the paper.</p> <p>The overall structure is defined in the configuration files (e.g., `extract_ast.yaml` and `extract_late_fusion.yaml`):</p> <p>```<br>.<br>├── dataset # MIMII Dataset (Input data structure)<br>├── cache # Stores PTM embeddings (HDF5 files)<br>├── model # Stores trained Autoencoders (.pth)<br>└── result # Stores final YAML/CSV results<br> └── results_fusion # Fusion summary and plots (CKA, Figures)<br>``</p> <p>#All code in code.zip .</p> <p>#All data is the result of running the code in cache.zip, model.zip, results.zip.</p> |
| title | Quantifying PTM-detector geometric alignment for reconstruction-based anomalous sound detection |
| url | https://doi.org/10.5281/zenodo.17489579 |