What strategies would you use to fine-tune a pretrained VAE for anomaly detection tasks

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Can you tell me some strategies that you use to fine-tune a pre-trained VAE for anomaly detection tasks?
Jan 15 in Generative AI by Ashutosh
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1 answer to this question.

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Strategies for Fine-Tuning a Pretrained VAE for Anomaly Detection:

  1. Transfer Learning: Use the pretrained encoder and decoder weights as initialization for the VAE and fine-tune on the target dataset.
  2. Reconstruction Loss: Focus on minimizing reconstruction error to differentiate normal and anomalous samples.
  3. Latent Space Regularization: Fine-tune the latent space to better capture the distribution of normal data.
  4. Custom Loss Functions: Introduce anomaly-specific loss functions, such as higher penalties for reconstruction errors on anomalous data.
  5. Data Augmentation: Use augmentation techniques to improve robustness and avoid overfitting.

In the above code we are using the following key points:

  • Reconstruction Error: Detect anomalies by setting a threshold for reconstruction loss.
  • Latent Space Refinement: Fine-tune the latent space to better represent normal data distribution.
  • Transfer Learning: Save training time by leveraging pre-trained weights.
  • Custom Thresholding: Adjust thresholds based on validation data to improve anomaly detection accuracy.
Hence, by referring to above you can fine-tune a pretrained VAE for anomaly detection tasks.
answered Jan 17 by nidhi jha

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