Efficient Baseline

Efficient baselines in machine learning research focus on developing simple, fast, and easily reproducible models that achieve strong performance, serving as robust benchmarks for evaluating more complex approaches. Current research emphasizes streamlined architectures like lightweight U-Nets and improved implementations of existing methods (e.g., Faster R-CNN, HRNet), often incorporating attention mechanisms or graph neural networks where appropriate. This focus on efficiency is driven by the need to reduce computational costs and improve accessibility, enabling faster iteration and more rigorous comparisons across diverse applications ranging from weather forecasting to autonomous driving.