To apply data-centric approaches in Variational Autoencoders (VAE) to generate realistic synthetic datasets for training machine learning models, you can follow the following steps:
- Data Augmentation: Use VAEs to generate diverse samples that augment the training set, helping models generalize better by exposing them to varied patterns.
- Latent Space Regularization: Regularize the latent space to ensure that generated data points cover a wide and balanced range of the input space, improving dataset diversity.
- Domain-Specific Priors: Introduce domain-specific priors in the VAE to generate realistic data that matches the distribution of the real-world dataset.
- Consistency with Real Data: Implement a reconstruction loss that ensures generated synthetic data is consistent with real-world data distributions.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Data Augmentation: VAE generates synthetic data to augment the real training dataset.
- Latent Space Regularization: Ensures realistic and diverse data generation through regularization of the latent space.
- Domain-Specific Priors: By conditioning on domain knowledge, the VAE can generate more relevant synthetic data.
- Consistency with Real Data: The reconstruction loss ensures the synthetic data aligns with the real data distribution.
Hence, by referring to the above, you can apply data-centric approaches in VAE models to generate realistic synthetic datasets for training machine learning models.
