[GSoC 2025] Explore Transformer-Based Architectures #18
Description
Background
Our current diffusion model for DIA-MS data deconvolution uses a U-Net-based architecture. While effective, recent advances in Vision Transformers (ViTs) and other transformer-based architectures have shown promising results in many domains. This task involves exploring whether transformer-based architectures could provide performance improvements for our specific use case of MS signal deconvolution. We have previously tried a custom transformer backbone.
Task Objectives
- Implement one or more transformer-based architectures (e.g., ViT, Swin Transformer) as alternative backbones for our diffusion model
- Adapt these architectures to work with our 1D/2D MS data representation
- Train and evaluate the transformer-based models against our U-Net baseline
- Analyze trade-offs in performance, training time, and resource requirements
Technical Details
- MS data has unique characteristics that may require architectural adaptations of standard transformer models
- Consider attention mechanisms specifically suited for spectral data
- Focus initially on smaller transformer variants to enable rapid experimentation
Deliverables
- Implementation of at least one transformer-based architecture compatible with our existing pipeline
- Training and evaluation scripts for the new architecture(s)
- Performance comparison with our current U-Net baseline, including:
- Deconvolution quality metrics
- Convergence speed
- Inference time
- Memory requirements
- Analysis of the strengths and weaknesses of different architectures for our specific problem
Resources
- Current model implementation in our repository
- Vision Transformer documentation
Difficulty
Advanced - This task requires deep understanding of model architectures and their adaptation to specialized data formats.