Understanding and Simplifying Perceptual Distances

no code implementations • CVPR 2021 • Dan Amir, Yair Weiss

Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classical losses such as L1 or L2 in pixel space.

Perceptual Distance

Theory of Curriculum Learning, with Convex Loss Functions

no code implementations • 9 Dec 2018 • Daphna Weinshall, Dan Amir

We also prove that when the ideal difficulty score is fixed, the convergence rate is monotonically increasing with respect to the loss of the current hypothesis at each point.

Binary Classification

Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

no code implementations • ICML 2018 • Daphna Weinshall, Gad Cohen, Dan Amir

We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss.

Learning Theory Transfer Learning