Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision
A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages range from guarantees for statistical generalization to better accuracy to compression. But support for general constraints within widely used libraries remains scarce and their broader deployment within many applications that can benefit from them remains under-explored.
In this project, we revisit a classical first order scheme from numerical optimization, Conditional Gradients (CG), that has, thus far had limited applicability in training deep models.
More details about the project can be found in arxiv
See resnet-in-tensorflow for the resnet experiments
See dcgan-completion.tensorflow for GAN experiments
See path-sgd for path-norm experiments
See norms.py for various CG updates for simple constrained problems mentioned in the main paper.