Effective Baseline

Effective baselines in machine learning research aim to establish simple, well-performing models that serve as robust benchmarks for evaluating more complex approaches. Current research focuses on developing and applying these baselines across diverse areas, including audio enhancement, depth estimation, reinforcement learning, and image/video processing, often revealing that surprisingly simple methods can achieve state-of-the-art or near state-of-the-art results. This emphasis on strong baselines improves the rigor and reproducibility of research, facilitating more accurate comparisons and ultimately accelerating progress in various fields by providing a reliable foundation for evaluating novel algorithms and techniques.