Summary of Minimaxad: a Lightweight Autoencoder For Feature-rich Anomaly Detection, by Fengjie Wang et al.
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
by Fengjie Wang, Chengming Liu, Lei Shi, Pang Haibo
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MiniMaxAD model addresses challenges in unsupervised anomaly detection (UAD) by efficiently compressing and memorizing information from normal images. The lightweight autoencoder employs a feature diversity enhancement technique, large kernel convolution, and an Adaptive Contraction Hard Mining Loss (ADCLoss) tailored to FRADs. These innovations enable the model to extract abstract patterns, achieving state-of-the-art performance in multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MiniMaxAD is a new way to find unusual things in pictures without being told what’s normal first. The problem with old methods was that they got confused when trying to learn from many different kinds of pictures. To fix this, the MiniMaxAD team created a special kind of computer program that can see patterns in pictures and remember lots of details. This helps the model find weird things even better than before. It’s really good at finding unusual things in many types of pictures! |
Keywords
» Artificial intelligence » Anomaly detection » Autoencoder » Unsupervised
