Скачать или смотреть Understand How Autoencoder detects Anomaly

*Anomaly Detection Using Autoencoders (Deep Learning)*
Autoencoders are neural networks trained to reconstruct input data. Anomalies are detected based on **reconstruction error**—since the model learns normal patterns, it struggles to reconstruct anomalies accurately, leading to high error.


*3. Autoencoder Variants for Anomaly Detection*
1. *Vanilla Autoencoder* (Basic dense layers).
2. *Convolutional Autoencoder (CAE)* – Better for image data.
3. *LSTM Autoencoder* – For sequential/time-series data.
4. *Variational Autoencoder (VAE)* – Probabilistic approach.
5. *Denoising Autoencoder* – Trained on corrupted inputs for robustness.

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*4. Pros & Cons*
✅ **Pros**:
Works well for high-dimensional data (images, time series).
Unsupervised (no need for labeled anomalies).

❌ **Cons**:
Requires mostly normal training data.
May struggle if anomalies are too similar to normal data.


*6. When to Use Autoencoders?*
*High-dimensional data* (e.g., images, sensor data).
*Unsupervised setting* (no labeled anomalies).
**When traditional methods (LOF, Isolation Forest) fail**.

Would you like help adapting this for a specific dataset (e.g., images, time series)?