Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
Authors: Sukanya Patra, Souhaib Ben Taieb
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation across five benchmark datasets demonstrates the performance of ULSAD in detecting and localizing both structural and logical anomalies, outperforming eight state-of-the-art methods. An extensive ablation study further highlights the contribution of each component to the overall performance improvement. |
| Researcher Affiliation | Academia | Sukanya Patra EMAIL University of Mons Souhaib Ben Taieb EMAIL Mohamed bin Zayed University of Artificial Intelligence University of Mons |
| Pseudocode | Yes | Algorithm 1: Unified Logical and Structural AD (ULSAD) |
| Open Source Code | Yes | Our code is available at https://github.com/sukanyapatra1997/ULSAD-2024.git. |
| Open Datasets | Yes | [1] BTAD (Mishra et al., 2021). [2] MVTec AD (Bergmann et al., 2019). [3] MVTec-Loco (Bergmann et al., 2022). [4] MPDD (Jezek et al., 2021). [5] Vis A (Zou et al., 2022). |
| Dataset Splits | Yes | The training and validation sets contains only normal samples, i.e., y = 0. For the sake of simplicity, we refer to the training set as DN = {X | (X, 0) Dtrain}. The test set Dtest includes both normal and anomalous samples... [MVTec-Loco] It consists of 5 categories, with 1,772 normal images for training and 304 normal images for validation. It also contains 1568 images, either normal or anomalous, for evaluation. |
| Hardware Specification | Yes | For this analysis, we ran inference on the test samples in the MVTec LOCO dataset using an NVIDIA A100 GPU... Moreover, we used a single NVIDIA A4000 GPU for all the experiments unless mentioned otherwise. |
| Software Dependencies | No | ULSAD is implemented in Py Torch (Paszke et al., 2019). For the baselines, we follow the implementation in Anomalib (Akcay et al., 2022), a widely used AD library for benchmarking. |
| Experiment Setup | Yes | We train ULSAD over 200 epochs for each category using an Adam optimizer with a learning rate of 0.0002 and a weight decay of 0.00002. We set α = 0.9 and β = 0.995 unless specified otherwise. |