Papers › Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
Zahra Dehghanian, Saeed Saravani, Maryam Amirmazlaghani, Mohammad Rahmati
This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previous methods suffer from the high variance between class-wise accuracy which leads to not being applicable for all types of anomalies. The proposed method named RCALAD tries to solve this problem by introducing a novel discriminator to the structure, which results in a more efficient training process. Additionally, RCALAD employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution, effectively separating anomalous samples from their reconstructions and facilitating more accurate anomaly detection. To further enhance the performance of the model, two novel anomaly scores are introduced. The proposed model has been thoroughly evaluated through extensive experiments on six various datasets, yielding results that demonstrate its superiority over existing state-of-the-art models. The code is readily available to the research community at https://github.com/zahraDehghanian97/RCALAD.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | CIFAR-10 | RCALAD | Mean AUC | 65.7 | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | KDD Cup 1999 | RCALAD | F1-Score | 95.4 | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | MIT-BIH Arrhythmia Database | RCALAD | F1 score | 60.6 | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | Musk v1 | RCALAD | F1-Score | 63.1 | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | SVHN | RCALAD | Mean AUC | 57.7 | #1 of 1 | Archive leaderboard | report |
| Anomaly Detection | Thyroid | RCALAD | F1-Score | 52.6 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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