Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.
Delves into convolutional filters as an inductive bias for images in neural networks, emphasizing independence to translation and local feature detectors.
Delves into the mathematical foundations and importance of directional cues in image processing, exploring computational challenges and selectivity to orientation.