Name
Abstract | ||
|---|---|---|
In this work, we propose CBiGAN - a novel method Reconstruction Difference for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD - a real-world benchmark for unsupervised anomaly detection on high-resolution images - and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin and performs comparably to expensive state-of-the-art iterative methods while reducing the computational cost. We also observe that our model is particularly effective in texture-type anomaly detection, as it sets a new state of the art in this category. Our code is available at https://github.com/fabiocarrara/cbigan-ad/. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPR48806.2021.9412253 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
DocType | ISSN | Citations |
Conference | 1051-4651 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio Carrara | 1 | 29 | 8.17 |
| Giuseppe Amato | 2 | 505 | 106.68 |
| Luca Brombin | 3 | 0 | 0.34 |
| Fabrizio Falchi | 4 | 459 | 55.65 |
| Claudio Gennaro | 5 | 490 | 57.23 |