Summary of Improved Autoencoder with Lstm Module and Kl Divergence, by Wei Huang et al.
Improved AutoEncoder with LSTM module and KL divergence
by Wei Huang, Bingyang Zhang, Kaituo Zhang, Hua Gao, Rongchun Wan
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel anomaly detection model, Improved AutoEncoder with LSTM module and Kullback-Leibler divergence (IAE-LSTM-KL), to tackle the challenges of over-reconstruction in deep convolutional autoencoder (CAE) networks and feature collapse in deep supporting vector data description (SVDD) models. The IAE-LSTM-KL model combines the strengths of CAE and SVDD by adding an LSTM module after the encoder to memorize normal data features, while also mitigating feature collapse through KL divergence penalization. Experimental results on synthetic and real-world datasets show that IAE-LSTM-KL achieves higher detection accuracy for anomalies and enhanced robustness to contaminated outliers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new model called IAE-LSTM-KL that helps computers find unusual data points in a group of data. The old models, CAE and SVDD, had problems finding all the weird data points. So, the researchers added an extra part to their model, like a memory keeper, to help it remember what normal data looks like. They also made some changes to make sure the model doesn’t get stuck in a pattern. Then, they tested the new model on some fake and real data sets and found that it works better than the old models. |
Keywords
» Artificial intelligence » Anomaly detection » Autoencoder » Encoder » Lstm
