CS Talk

Computing Science Academic Literature Reading Group

Our interests include:

  • Artificial Intelligence
  • Parallel and Distributed Systems
  • Statistics
  • Software Development Processes and Practices
  • Algorithms

Latest discussions

2017-12-12

Model Compression

Authors

Cristian Bucila, Rich Caruana, Alexandru Niculescu-Mizil

Abstract

Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hearing aids). We present a method for "compressing" large, complex ensembles into smaller, faster models, usually without significant loss in performance.

Discussion Notes

2017-04-29

Deep Photo Style Transfer

Authors

Fujun Luan, Sylvain Paris, Eli Shechtman, Kavita Bala

Abstract

This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.

Discussion Notes