• Corpus ID: 252907351

Towards Device Efficient Conditional Image Generation

  title={Towards Device Efficient Conditional Image Generation},
  author={Nisarg A. Shah and Gaurav Bharaj},
We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder without sacrificing quality of photo-realistic image generation. Our method is device agnostic, and can optimize an autoencoder for a given CPU-only, GPU compute device(s) in about normal time it takes to train an autoencoder on a generic workstation. We achieve this via a two-stage novel strategy where, first, we condense the channel weights, such that, as few as possible channels are… 



GAN Compression: Efficient Architectures for Interactive Conditional GANs

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Co-Evolutionary Compression for Unpaired Image Translation

  • Han ShuYunhe Wang Chang Xu
  • Computer Science
    2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2019
A novel co-evolutionary approach for reducing their memory usage and FLOPs simultaneously and synergistically optimized for investigating the most important convolution filters iteratively is developed.

A Style-Aware Content Loss for Real-time HD Style Transfer

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A Neural Algorithm of Artistic Style is introduced that can separate and recombine the image content and style of natural images and provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.

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