Ground Truths in Algorithm Formation

This lecture delves into the importance of ground truths in shaping new algorithms, focusing on the face detection and saliency detection domains. The instructor discusses the challenges and recent developments in statistical learning, emphasizing the critical role of benchmarked datasets. Through exercises and discussions, students explore the trajectory of ground truths supporting academic articles, analyzing their accessibility, construction, and implications. The session also covers the impact of biases in algorithm development and the need for diverse datasets. Practical examples and methodologies are presented to illustrate the process of utilizing ground truths in algorithmic research.