Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
This lecture discusses the development of more efficient architectures for saliency prediction by leveraging deep representations pre-trained for object recognition. The instructor presents a greedy pruning method called Fisher pruning, combined with knowledge distillation, to achieve faster single-image gaze prediction. The lecture emphasizes the importance of speeding up gaze prediction for real-world applications and video saliency models. Additionally, the lecture explores the challenges and recent developments in statistical learning, bias, ground truth, and ethics in critical data studies.