UNet Architecture: Enhanced AutoEncoder with Residual Connections
UNet is a sophisticated neural network architecture that builds upon the AutoEncoder concept by incorporating Residual Connection. Originally designed for image segmentation, it represents an architectural pattern rather than a specific model implementation, similar to how Seq2Seq defines a broader framework.
Key Characteristics:
- Symmetric Design
- Features a U-shaped architecture with matching encoder and decoder paths
- Implements systematic downsampling and upsampling through contracting and expanding paths
- Advanced Features
- Utilizes Residual Connection between corresponding compressed and decompressed layers
- Effectively captures multi-scale image features while preserving critical information during progressive noise introduction
- Modern Implementations
- Transformer-based variants incorporate denoising tokens with random noise injection
