In the paper "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", the authors talk about using:
- Feature Pyramid Networks (as the architecture)
- EfficientNet-B2 (as the backbone)
Performance Measures on the Validation Set. The RF model that only inputs data from the visible Landsat 8 bands achieved the lowest performance on the validation set, but the incorporation of auxiliary predictors substantially improved its performance. All of the CNN models outperformed the RF models. The best performing model, which we call ForestNet, used an FPN architecture with an EfficientNet-B2 backbone. The use of SDA provided large performance gains on the validation set, and land cover pre-training and incorporating auxiliary predictors each led to additional performance improvements.
What's the difference between architectures and backbones? I can't find much online. Specifically, what are their respective purposes? From a high-level perspective, what would integrating the two look like?