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cGANs for Cartoon to Real-life Images

arXiv 2101.09793 ↗First posted 24 Jan 2021cs.CV eess.IVcited by 0

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Abstract (arXiv)

The image-to-image translation is a learning task to establish a visual mapping between an input and output image. The task has several variations differentiated based on the purpose of the translation, such as synthetic to real translation, photo to caricature translation, and many others. The problem has been tackled using different approaches, either through traditional computer vision methods, as well as deep learning approaches in recent trends. One approach currently deemed popular and effective is using the conditional generative adversarial network, also known shortly as cGAN. It is adapted to perform image-to-image translation tasks with typically two networks: a generator and a discriminator. This project aims to evaluate the robustness of the Pix2Pix model by applying the Pix2Pix model to datasets consisting of cartoonized images. Using the Pix2Pix model, it should be possible to train the network to generate real-life images from the cartoonized images.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 24 Jan 2021.

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14 of 25 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  4. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  5. 2014Conditional Generative Adversarial Netscited by 2,152
  6. 2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,337
  7. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  8. 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
  9. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  10. 2019Domain Adaptation for Structured Output via Discriminative Patch Representationscited by 150
  11. 2017Virtual to Real Reinforcement Learning for Autonomous Drivingcited by 79
  12. 2017Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptationcited by 22
2 more
  1. 2017Unpaired Photo-to-Caricature Translation on Faces in the Wildcited by 3
  2. 2016Systematic evaluation of CNN advances on the ImageNetcited by 2

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