Summary of Meliad: Interpretable Few-shot Anomaly Detection with Metric Learning and Entropy-based Scoring, by Eirini Cholopoulou et al.
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring
by Eirini Cholopoulou, Dimitris K. Iakovidis
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel methodology for interpretable anomaly detection called MeLIAD is proposed, which addresses challenges in deep learning (DL) models used in multimedia applications. These models typically require large-scale annotated data, are highly imbalanced due to the scarcity of anomalies, and lack transparency. MeLIAD uses metric learning and achieves interpretability by design without relying on prior distribution assumptions. It requires only a few samples of anomalies for training, doesn’t use augmentation techniques, and provides visualizations that offer insights into why an image is identified as anomalous. This is achieved through a trainable entropy-based scoring component and a novel loss function that optimizes the anomaly scoring with a metric learning objective. Experiments on five public benchmark datasets demonstrate improved performance compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of finding weird pictures in multimedia applications uses a special method called MeLIAD. It’s hard to train because there are only a few examples of weird pictures, and the machine learning models don’t show why they’re weird. MeLIAD makes it easier by using a different type of learning that doesn’t need as much data or extra help from people. This helps us understand why the model thinks a picture is weird. It’s tested on many datasets and does better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Loss function » Machine learning
