Difference between revisions of "Image-to-Image Translation"

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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
 
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
 
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}}
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[http://www.youtube.com/results?search_query=CycleGAN YouTube search...]
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[https://www.youtube.com/results?search_query=CycleGAN YouTube search...]
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[http://www.google.com/search?q=CycleGAN ...Google search]
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[https://www.google.com/search?q=CycleGAN ...Google search]
  
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* [[Video/Image]]
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* [https://towardsdatascience.com/image-to-image-translation-69c10c18f6ff Image-to-Image Translation | Yongfu Hao]
 
* [[Generative Adversarial Network (GAN)]]
 
* [[Generative Adversarial Network (GAN)]]
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* [[Generated Image]]
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* [[Image Classification]]
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* [[http://towardsdatascience.com/cyclegan-learning-to-translate-images-without-paired-training-data-5b4e93862c8d CycleGAN: Learning to Translate Images (Without Paired Training Data) | Sarah Wolf - Towards Data Science]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]]
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* [https://github.com/weihaox/awesome-image-translation Awesome Image-To-Image Translation Papers | GitHub]
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Image-to-image translation is the controlled conversion of a given source image to a target image. Examples might be the conversion of day image to night image, or black and white photographs to color photographs.
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<youtube>sIkUzmgUaxc</youtube>
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<youtube>NsPMlDsRCkM</youtube>
  
 
Approaches:
 
Approaches:
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* Unparied
 
* Unparied
  
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Image to image translation
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https://miro.medium.com/max/650/0*P-46iNsLcF2edVfn.png
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= StarGAN =
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Existing image to image translation approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. StarGAN is a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. [https://towardsdatascience.com/image-to-image-translation-69c10c18f6ff Image-to-Image Translation | Yongfu Hao]
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https://miro.medium.com/max/648/0*S7N84-uT_6zqrhxl.png
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<youtube>8XfcDkkFbMs</youtube>
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<youtube>u8qPvzk0AfY</youtube>
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= CycleGAN =
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* [https://www.semanticscholar.org/paper/Unpaired-Image-to-Image-Translation-Using-Networks-Zhu-Park/c43d954cf8133e6254499f3d68e45218067e4941 Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks | J. Zhu, T. Park, P. Isola, and A. Efros - Semantic Scholar]
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* [https://towardsdatascience.com/cyclegan-learning-to-translate-images-without-paired-training-data-5b4e93862c8d CycleGAN: Learning to Translate Images (Without Paired Training Data) | Sarah Wolf - Towards Data Science]
  
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An approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples [https://towardsdatascience.com/image-to-image-translation-69c10c18f6ff Image-to-Image Translation | Yongfu Hao]
  
 
<youtube>xkLtgwWxrec</youtube>
 
<youtube>xkLtgwWxrec</youtube>
 
<youtube>nB8uVGbesZ4</youtube>
 
<youtube>nB8uVGbesZ4</youtube>
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https://miro.medium.com/max/700/0*KXiC6nIcowYS5GtA.png
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= pix2pix GAN =
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* [https://machinelearningmastery.com/a-gentle-introduction-to-pix2pix-generative-adversarial-network/ A Gentle Introduction to Pix2Pix Generative Adversarial Network | Jason Brownlee - Machine Learning Mastery]
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<youtube>u7kQ5lNfUfg</youtube>
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<youtube>TVCZLb1qe_0</youtube>
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= tUNIT: Truly Unsupervised Image-to-Image Translation =
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* [https://arxiv.org/abs/2006.06500 Rethinking the Truly Unsupervised Image-to-Image Translation | K. Baek, Y. Choi, Y. Uh, J. Yoo, and H. Shim - arViv.org]
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* [https://github.com/clovaai/tunit clovaai/tunit | GitHub]
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<youtube>sEG8hD64c_Q</youtube>
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<youtube>mXrelAwwuhg</youtube>
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= dUNIT: Detection-Based Unsupervised Image-to-Image Translation =
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* [https://openaccess.thecvf.com/content_CVPR_2020/papers/Bhattacharjee_DUNIT_Detection-Based_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.pdf DUNIT: Detection-based Unsupervised Image-to-Image Translation | D. Bhattacharjee, S. Kim, G. Vizier, and M. Salzmann]
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<youtube>ENK1ROiZPms</youtube>
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= fUNIT: Few-Shot Unsupervised Image-to-Image Translation =
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* [https://arxiv.org/abs/1905.01723 Few-Shot Unsupervised Image-to-Image Translation | M. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, and J. Kautz]
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* [https://nvlabs.github.io/FUNIT/ Project page | GitHub]
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Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.
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<youtube>IhAsXcCz8LI</youtube>
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<youtube>ANwAhuOeaiE</youtube>
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https://nvlabs.github.io/FUNIT/images/animal_8x8.gif
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= UNIT: UNsupervised Image-to-image Translation =
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* [https://papers.nips.cc/paper/6672-unsupervised-image-to-image-translation-networks.pdf Unsupervised Image-to-Image Translation Networks | M. Liu, T. Breuel, and J. Kautz]
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* [https://github.com/mingyuliutw/UNIT mingyuliutw/UNIT | GitHub]
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<youtube>dqxqbvyOnMY</youtube>

Latest revision as of 20:16, 9 April 2024

YouTube search... ...Google search

Image-to-image translation is the controlled conversion of a given source image to a target image. Examples might be the conversion of day image to night image, or black and white photographs to color photographs.

Approaches:

  • Paired
  • Unparied

0*P-46iNsLcF2edVfn.png

StarGAN

Existing image to image translation approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. StarGAN is a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Image-to-Image Translation | Yongfu Hao

0*S7N84-uT_6zqrhxl.png


CycleGAN

An approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples Image-to-Image Translation | Yongfu Hao

0*KXiC6nIcowYS5GtA.png


pix2pix GAN


tUNIT: Truly Unsupervised Image-to-Image Translation


dUNIT: Detection-Based Unsupervised Image-to-Image Translation


fUNIT: Few-Shot Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.

animal_8x8.gif


UNIT: UNsupervised Image-to-image Translation

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