Variational Autoencoder (VAE)
Deep generative models for synthesizing medical images and RGB data using variational inference.
Explores reconstruction quality vs. latent-space smoothness, and evaluates stability issues such as posterior collapse.
Project Details
Technologies
The Problem
Generative modeling often suffers from "posterior collapse" or blurry outputs. In medical imaging, maintaining the structural integrity of the latent space is critical for downstream diagnostic tasks.
The Solution
Developed a robust VAE implementation utilizing the reparameterization trick. I experimented with various KL-divergence weightings to balance reconstruction quality with the smoothness of the latent space distribution.
Architecture & Implementation
- Framework: PyTorch for flexible deep learning experimentation.
- Encoder/Decoder: Deep convolutional architectures with residual connections.
- Datasets: Trained on both standard RGB datasets and specialized medical imaging (CT/X-ray) datasets.
Results & Impact
Achieved high-fidelity reconstructions and clear latent space interpolations, demonstrating the model's ability to learn meaningful low-dimensional representations of complex biological data.