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Mechanisms of Generative Image-to-Image Translation Networks

arXiv 2411.10368 ↗First posted 15 Nov 2024cs.CV cs.AI eess.IVcited by 0

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Abstract (arXiv)

Generative Adversarial Networks (GANs) are a class of neural networks that have been widely used in the field of image-to-image translation. In this paper, we propose a streamlined image-to-image translation network with a simpler architecture compared to existing models. We investigate the relationship between GANs and autoencoders and provide an explanation for the efficacy of employing only the GAN component for tasks involving image translation. We show that adversarial for GAN models yields results comparable to those of existing methods without additional complex loss penalties. Subsequently, we elucidate the rationale behind this phenomenon. We also incorporate experimental results to demonstrate the validity of our findings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 38 references that are in the map. First posted on arXiv 15 Nov 2024.

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26 of 38 references.

References · 26

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14 more
  1. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  2. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  3. 2015A Neural Algorithm of Artistic Stylecited by 483
  4. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  5. 2019DRIT++: Diverse Image-to-Image Translation via Disentangled Representationscited by 244
  6. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  7. 2017CVAE-GAN: Fine-Grained Image Generation through Asymmetric Trainingcited by 42
  8. 2017Perceptual Adversarial Networks for Image-to-Image Transformationcited by 42
  9. 2019TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translationcited by 40
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  12. 2022Generalization Error Bounds for Deep Neural Networks Trained by SGDcited by 14
  13. 2021TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasetscited by 7
  14. 2024Shape-Preserving Generation of Food Images for Automatic Dietary Assessmentcited by 1

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