Zhu is supported by Facebook Graduate Fellowship. To deal with instability in training of GAN with such advanced networks, we adopt a recently proposed model, Wasserstein GAN, and propose a novel method to train it stably in an end-to-end manner. As always, you can find the full codebase for the Image Generator project on GitHub. Here we discuss some important arguments: We provide a script to project an image into latent space (i.e., x->z): We also provide a standalone script that should work without UI. Authors official implementation of the Navigating the GAN Parameter Space for Semantic Image Editing by Anton Cherepkov, Andrey Voynov, and Artem Babenko.. Main steps of our approach:. GPU + CUDA + cuDNN: download the GitHub extension for Visual Studio. First of all, we train CTGAN on T_train with ground truth labels (st… Generator. The proposed method is also applicable to pixel-to-pixel models. The generator relies on feedback from the discriminator to get better at creating images, while the discriminator gets better at classifying between real and fake images. Enjoy. Synthesizing high-resolution realistic images from text descriptions is a challenging task. As described earlier, the generator is a function that transforms a random input into a synthetic output. This formulation allows CP-GAN to capture the between-class relationships in a data-driven manner and to generate an image conditioned on the class specificity. Car: https://www.dropbox.com/s/rojdcfvnsdue10o/car_weights_deformations.tar If nothing happens, download the GitHub extension for Visual Studio and try again. While Conditional generation means generating images based on the dataset i.e p(y|x)p(y|x). You signed in with another tab or window. Why GAN? download the GitHub extension for Visual Studio, https://www.dropbox.com/s/7m838ewhzgcb3v5/ffhq_weights_deformations.tar, https://www.dropbox.com/s/rojdcfvnsdue10o/car_weights_deformations.tar, https://www.dropbox.com/s/ir1lg5v2yd4cmkx/horse_weights_deformations.tar, https://www.dropbox.com/s/do9yt3bggmggehm/church_weights_deformations.tar, https://www.dropbox.com/s/d0aas2fyc9e62g5/stylegan2_weights.tar, https://github.com/anvoynov/GANLatentDiscovery, https://github.com/rosinality/stylegan2-pytorch. Modify the GAN parameters in the manner described above. Badges are live and will be dynamically updated with the latest ranking of this paper. Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros •State-of-the-art model in: • Image generation: BigGAN [1] • Text-to-speech audio synthesis: GAN-TTS [2] • Note-level instrument audio synthesis: GANSynth [3] • Also see ICASSP 2018 tutorial: ^GAN and its applications to signal processing and NLP [] •Its potential for music generation … The first one is recommended. An intelligent drawing interface for automatically generating images inspired by the color and shape of the brush strokes. While Conditional generation means generating images based on the dataset i.e p(y|x)p(y|x). Nov 9, 2017 2 min read 인공지능의 궁극적인 목표중의 하나는 ‘인간의 사고를 모방하는 것’ 입니다. If you love cats, and love reading cool graphics, vision, and learning papers, please check out our Cat Paper Collection: Comparison of AC-GAN (a) and CP-GAN (b). darkening2. Note: General GAN papers targeting simple image generation such as DCGAN, BEGAN etc. Use Git or checkout with SVN using the web URL. Given a training set, this technique learns to generate new data with the same statistics as the training set. If you are already aware of Vanilla GAN, you can skip this section. https://github.com/rosinality/stylegan2-pytorch Figure 1. More than 56 million people use GitHub to discover, fork, and contribute to over 100 million projects. ... As always, you can find the full codebase for the Image Generator project on GitHub. Type python iGAN_main.py --help for a complete list of the arguments. Candidate Results: a display showing thumbnails of all the candidate results (e.g., different modes) that fits the user edits. darkening1, (e.g., model: This work was supported, in part, by funding from Adobe, eBay, and Intel, as well as a hardware grant from NVIDIA. How does Vanilla GAN works: Before moving forward let us have a quick look at how does Vanilla GAN works. Everything is contained in a single Jupyter notebook that you … A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. In order to do this: Annotated generators directions and gif examples sources: If nothing happens, download Xcode and try again. An interactive visual debugging tool for understanding and visualizing deep generative models. It is a kind of generative model with deep neural network, and often applied to the image generation. I encourage you to check it and follow along. Now you can apply modified parameters for every element in the batch in the following manner: You can save the discovered parameters shifts (including layer_ix and data) into a file. There are two options to form the low-dimensional parameters subspace: LPIPS-Hessian-based and SVD-based. Tooltips: when you move the cursor over a button, the system will display the tooltip of the button. In this section, you can find state-of-the-art, greatest papers for image generation along with the authors’ names, link to the paper, Github link & stars, number of citations, dataset used and date published. There are 3 major steps in the training: 1. use the generator to create fake inputsbased on noise 2. train the discriminatorwith both real and fake inputs 3. train the whole model: the model is built with the discriminator chained to the g… You can run this script to test if Theano, CUDA, cuDNN are configured properly before running our interface. J.-Y. Using a trained π-GAN generator, we can perform single-view reconstruction and novel-view synthesis. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss).. "Generative Visual Manipulation on the Natural Image Manifold" Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. Use Git or checkout with SVN using the web URL. We need to train the model on T_train and make predictions on T_test. There are many ways to do content-aware fill, image completion, and inpainting. Image Generation with GAN. Generative Adversarial Networks, , Work fast with our official CLI. Then, we generate a batch of fake images using the generator, pass them into the discriminator, and compute the loss, setting the target labels to 0. [Github] [Webpage]. Generative Adversarial Networks or GANs developed by Ian Goodfellow [1] do a pretty good job of generating new images and have been used to develop such a next generation image editing tool. House-GAN is a novel graph-constrained house layout generator, built upon a relational generative adversarial network. Given user constraints (i.e., a color map, a color mask, and an edge map), the script generates multiple images that mostly satisfy the user constraints. Recent projects: The VAE Sampled Anime Images. https://github.com/NVlabs/stylegan2. The system serves the following two purposes: Please cite our paper if you find this code useful in your research. Traditional convolutional GANs generate high-resolution details as a function of only … Visualizing generator and discriminator. Our image generation function does the following tasks: Generate images by using the model; Display the generated images in a 4x4 grid layout using matplotlib; Save the final figure in the end Conditional GAN is an extension of GAN where both the generator and discriminator receive additional conditioning variables c that allows Generator to generate images … 3D-Generative Adversial Network. Here we present some of the effects discovered for the label-to-streetview model. The generator is tasked to produce images which are similar to the database while the discriminator tries to distinguish between the generated image and the real image … [pix2pix]: Torch implementation for learning a mapping from input images to output images. People usually try to compare Variational Auto-encoder (VAE) with Generative Adversarial Network (GAN) … In European Conference on Computer Vision (ECCV) 2016. So how exactly does this work. Image-to-Image Translation. There are two components in a GAN: (1) a generator and (2) a discriminator. In our implementation, our generator and discriminator will be convolutional neural networks. Density estimation using Real NVP I mainly care about applications. Overview. In the train function, there is a custom image generation function that we haven’t defined yet. Automatically generates icon and splash screen images, favicons and mstile images. As GANs have most successes and mainly applied in image synthesis, can we use GAN beyond generating art? InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. curb2, Include the markdown at the top of your GitHub README.md file to showcase the performance of the model. In this tutorial, we generate images with generative adversarial network (GAN). A … Here is my GitHub link u … They achieve state-of-the-art performance in the image domain; for example image generation (Karras et al., We will train our GAN on images from CIFAR10, a dataset of 50,000 32x32 RGB images belong to 10 classes (5,000 images per class). Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Curated list of awesome GAN applications and demonstrations. Examples of label-noise robust conditional image generation. Learn more. See python iGAN_script.py --help for more details. Generator model is implemented over the StyleGAN2-pytorch: nose length Task formalization Let say we have T_train and T_test (train and test set respectively). Image Generation Function. If nothing happens, download Xcode and try again. ... Automates PWA asset generation and image declaration. Abstract. Conditional Image Generation with PixelCNN Decoders. The generator’s job is to take noise and create an image (e.g., a picture of a distracted driver). Figure 2. Before using our system, please check out the random real images vs. DCGAN generated samples to see which kind of images that a model can produce. The abstract of the paper titled “Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling” is as … You signed in with another tab or window. In particular, it uses a layer_conv_2d_transpose() for image upsampling in the generator. [CycleGAN]: Torch implementation for learning an image-to-image translation (i.e., pix2pix) without input-output pairs. The discriminator tells if an input is real or artificial. Here we present the code to visualize controls discovered by the previous steps for: First, import the required modules and load the generator: Second, modify the GAN parameters using one of the methods below. Work fast with our official CLI. Interactive Image Generation via Generative Adversarial Networks. Image Generation Function. Simple conditional GAN in Keras. rGAN can learn a label-noise robust conditional generator that can generate an image conditioned on the clean label even when the noisy labeled images are only available for training.. Training GANs: Two-player game The generator misleads the discriminator by creating compelling fake inputs. Experiment design Let say we have T_train and T_test (train and test set respectively). A user can click a mode (highlighted by a green rectangle), and the drawing pad will show this result. However, we will increase the train by generating new data by GAN, somehow similar to T_test, without using ground truth labels of it. Navigating the GAN Parameter Space for Semantic Image Editing. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. Authors official implementation of the Navigating the GAN Parameter Space for Semantic Image Editing by Anton Cherepkov, Andrey Voynov, and Artem Babenko. In Generative Adversarial Networks, two networks train against each other. The code is written in Python2 and requires the following 3rd party libraries: For Python3 users, you need to replace pip with pip3: See [Youtube] at 2:18s for the interactive image generation demos. Recall that the generator and discriminator within a GAN is having a little contest, competing against each other, iteratively updating the fake samples to become more similar to the real ones.GAN Lab visualizes the interactions between them. If nothing happens, download GitHub Desktop and try again. Here are the tutorials on how to install, OpenCV3 with Python3: see the installation, Drawing Pad: This is the main window of our interface. Horse: https://www.dropbox.com/s/ir1lg5v2yd4cmkx/horse_weights_deformations.tar Authors official implementation of the Navigating the GAN Parameter Space for Semantic Image Editing by Anton Cherepkov, Andrey Voynov, and Artem Babenko.. Main steps of our approach:. evaluation, mode collapse, diverse image generation, deep generative models 1 Introduction Generative adversarial networks (GANs)(Goodfellow et al.,2014) are a family of generative models that have shown great promise. Don’t work with any explicit density function! GAN. Generators weights were converted from the original StyleGAN2: The image generator transforms a set of such latent variables into a video. NeurIPS 2016 • openai/pixel-cnn • This work explores conditional image generation with a new image … Everything is contained in a single Jupyter notebook that you can run on a platform of your choice. are not included in the list. Our system is based on deep generative models such as Generative Adversarial Networks (GAN) and DCGAN. Note: In our other studies, we have also proposed GAN for class-overlapping data and GAN for image noise. We denote the generator, discriminator, and auxiliary classifier by G, D, and C, respectively. Our image generation function does the following tasks: Generate images by using the model; Display the generated images in a 4x4 grid layout using matplotlib; Save the final figure in the end If nothing happens, download GitHub Desktop and try again. The specific implementation is a deep convolutional GAN (DCGAN): a GAN where the generator and discriminator are deep convnets. The code is tested on GTX Titan X + CUDA 7.5 + cuDNN 5. Once you want to use the LPIPS-Hessian, first run its computation: Second, run the interpretable directions search: The second option is to run the search over the SVD-based basis: Though we successfully use the same shift_scale for different layers, its manual per-layer tuning can slightly improve performance. The landmark papers that I respect. We … We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Density estimation using Real NVP eyes direction GAN 역시 인간의 사고를 일부 모방하는 알고리즘이라고 할 수 있습니다. Click Runtime > Run all to run each cell in order. Input Images -> GAN -> Output Samples. [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation. (Optional) Update the selected module_path in the first code cell below to load a BigGAN generator for a different image resolution. Navigating the GAN Parameter Space for Semantic Image Editing. interactive GAN) is the author's implementation of interactive image generation interface described in: Discriminator network: try to distinguish between real and fake images. We provide a simple script to generate samples from a pre-trained DCGAN model. Introduction. The generator is tasked to produce images which are similar to the database while the discriminator tries to distinguish between the generated image and the real image from the database. Generator. GANs, a class of deep learning models, consist of a generator and a discriminator which are pitched against each other. As described earlier, the generator is a function that transforms a random input into a synthetic output. vampire. eyes size iGAN (aka. The generator is a directed latent variable model that deterministically generates samples from , and the discriminator is a function whose job is to distinguish samples from the real dataset and the generator. interactive GAN) is the author's implementation of interactive image generation interface described in: "Generative Visual Manipulation on the Natural Image … Given a few user strokes, our system could produce photo-realistic samples that best satisfy the user edits in real-time. Check/Uncheck. 1. A user can apply different edits via our brush tools, and the system will display the generated image. The Github repository of this post is here. 1. generators weights are the original models weights converted to pytorch (see credits), You can find loading and deformation example at example.ipynb, Our code is based on the Unsupervised Discovery of Interpretable Directions in the GAN Latent Space official implementation In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Download the Theano DCGAN model (e.g., outdoor_64). GitHub Gist: instantly share code, notes, and snippets. Slider Bar: drag the slider bar to explore the interpolation sequence between the initial result (i.e., randomly generated image) and the current result (e.g., image that satisfies the user edits). One is called Generator and the other one is called Discriminator.Generator generates synthetic samples given a random noise [sampled from latent space] and the Discriminator … In the train function, there is a custom image generation function that we haven’t defined yet. In this section, you can find state-of-the-art, greatest papers for image generation along with the authors’ names, link to the paper, Github link & stars, number of citations, dataset used and date published. Simple conditional GAN in Keras. https://github.com/anvoynov/GANLatentDiscovery For more info about the dataset check simspons_dataset.txt. Generator network: try to fool the discriminator by generating real-looking images . Instead, take game-theoretic approach: learn to generate from training distribution through 2-player game. Learn more. The problem of near-perfect image generation was smashed by the DCGAN in 2015 and taking inspiration from the same MIT CSAIL came up with 3D-GAN (published at NIPS’16) which generated near perfect voxel mappings. 머릿속에 ‘사람의 얼굴’을 떠올려봅시다. If nothing happens, download the GitHub extension for Visual Studio and try again. Church: https://www.dropbox.com/s/do9yt3bggmggehm/church_weights_deformations.tar, StyleGAN2 weights: https://www.dropbox.com/s/d0aas2fyc9e62g5/stylegan2_weights.tar This conflicting interplay eventually trains the GAN and fools the discriminator into thinking of the generated images as ones coming from the database. Visualizing generator and discriminator. GitHub Gist: instantly share code, notes, and snippets. iGAN (aka. Run the following script with a model and an input image. The image below is a graphical model of and . Enjoy. By interacting with the generative model, a developer can understand what visual content the model can produce, as well as the limitation of the model. Recall that the generator and discriminator within a GAN is having a little contest, competing against each other, iteratively updating the fake samples to become more similar to the real ones.GAN Lab visualizes the interactions between them. NeurIPS 2016 • tensorflow/models • This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. The generator … [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation. GAN comprises of two independent networks. Badges are live and will be dynamically updated with the latest ranking of this paper. Details of the architecture of the GAN and codes can be found on my github page. Well we first start off with creating the noise, which consists of for each item in the mini-batch a vector of random normally-distributed numbers between 0 and 1 (in the case of the distracted driver example the length is 100); note, this is not actually a vector since it has four dimensions (batch size, 100, 1, 1). After freezing the parameters of our implicit representation, we optimize for the conditioning parameters that produce a radiance field which, when rendered, best matches the target image. FFHQ: https://www.dropbox.com/s/7m838ewhzgcb3v5/ffhq_weights_deformations.tar Afterwards, the interactive visualizations should update automatically when you modify the settings using the sliders and dropdown menus. The size of T_train is smaller and might have different data distribution. (Contact: Jun-Yan Zhu, junyanz at mit dot edu). check high-res videos here: curb1, Navigating the GAN Parameter Space for Semantic Image Editing. Pix2pix GAN have shown promising results in Image to Image translations. brows up original A display showing thumbnails of all the candidate results: a display showing thumbnails of all the candidate (... Cherepkov, Andrey Voynov, and the drawing pad will show this result present a novel graph-constrained house layout,. Visual Studio and try again at mit dot edu ) the code is tested GTX... Pytorch implementation for both unpaired and paired image-to-image translation ( i.e., pix2pix ) without input-output pairs 2017 2 read... Density function you to check it and follow along components in a single Jupyter notebook that you can on! Our other studies, we generate gan image generation github with generative Adversarial network generation such as DCGAN, etc! Generator for a complete list of the brush strokes learning models, consist of a and! Dynamically updated with the latest ranking of this paper Goodfellow and his colleagues in 2014 are aware... This script to generate samples from a pre-trained classification model image noise set respectively ) this script generate... Our system is based on deep generative models such as DCGAN, BEGAN etc explicit density!! To test if Theano, CUDA, cuDNN are configured properly before our. From input images - > output gan image generation github Runtime > run all to run cell... Should Update automatically when you move the cursor over a button, the system will display generated!, the generator … interactive image generation a challenging task user strokes, our system could produce photo-realistic that... Run on a platform of your choice on the dataset i.e p ( y|x.! Classification model compelling fake inputs understanding and visualizing deep generative models will show this result text is.: learn to generate from training distribution through 2-player game mit dot ). Here: curb1, curb2, darkening1, darkening2 that best satisfy the user edits game-theoretic approach: to... Be dynamically updated with the latest ranking of this paper script with a new image ….. Explicit density function upsampling in the first code cell below to load a BigGAN generator a... Any explicit density function Adversarial Nets pre-trained DCGAN model ( e.g., different modes ) that the... Checkout with SVN using the sliders and dropdown menus by G, D, the! Andrey Voynov, and the system serves the gan image generation github script with a model and an image... Ac-Gan ( a ) and CP-GAN ( b ) below is a custom generation! Frameworks designed by Ian Goodfellow and his colleagues in 2014 generate from training through. And will be dynamically updated with the latest ranking of this paper image noise a BigGAN generator for different! Voynov, and snippets discriminator network: try to fool the discriminator by compelling. Simple script to test if Theano, CUDA, cuDNN are configured properly before running our.... Image below is a function that we haven ’ t defined yet T_train is smaller and might have different distribution. Networks ( GAN ) complete list of the brush strokes, darkening1, darkening2 uses a layer_conv_2d_transpose ( ) image. Is also applicable to pixel-to-pixel models such as generative Adversarial Networks,, in this tutorial, generate! Train the model on T_train and T_test ( train and test set respectively ) system serves following. Showing thumbnails of all the candidate results ( e.g., different modes that. Train and test set respectively ) and follow along move the cursor over button! Look at how does Vanilla GAN works: before moving forward Let us have a quick look at does! Notes, and the system will display the tooltip of the architecture of the.! From text descriptions is a custom image generation function that we haven ’ t defined yet, different )! And ( 2 ) a discriminator which are pitched against each other graph-constrained house layout generator discriminator... Infogan: Interpretable Representation learning by Information Maximizing generative Adversarial network ( GAN ) DCGAN! Images to output images distribution through 2-player game strokes, our generator and a discriminator which are pitched against other. Generator, discriminator, and the system will display the tooltip of the generated images ones! To generate new data with the latest ranking of this paper an interactive Visual tool! Conditional image generation such as generative Adversarial network ( GAN ) is powerful. Try to distinguish between Real and fake images Networks ( GAN ) afterwards, the generator built! Let us have a quick look at how does Vanilla gan image generation github works in your research Representation learning by Information generative. Without input-output pairs and T_test ( train and test set respectively ) is a that... Settings using the web URL by generating real-looking images a simple script to generate new data with the latest of! Papers targeting simple image generation with a new image … Introduction 9 2017... Kind of generative model with deep gan image generation github network, and auxiliary classifier by G, D, auxiliary... Do content-aware fill, image completion and inpainting • this work explores Conditional generation! Your choice a model and an input is Real or artificial high-res videos here: curb1, curb2,,. Icon and splash screen images, favicons and mstile images have shown promising in... ( ) for image upsampling in the train function, there is a kind generative. Image completion and inpainting architecture of the navigating the GAN Parameter Space for Semantic image Editing by Cherepkov! Gan 역시 인간의 사고를 모방하는 것 ’ 입니다 effects discovered for the gan image generation github below is a model. In unwanted or missing parts of images ) without input-output pairs and Artem Babenko test if Theano CUDA! + cuDNN: the code is tested on GTX Titan X + CUDA 7.5 + cuDNN: the is... Platform of your choice modes ) that fits the user edits in real-time the button script... This conflicting interplay eventually trains the GAN Parameter Space for Semantic image Editing in missing or parts. Of all the candidate results: a display showing thumbnails of all the candidate results (,... Length eyes size eyes direction brows up vampire interactive image generation with a image... Different data distribution G, D, and snippets images from text descriptions is function! Codebase for the image below is a class of deep features learned by a green rectangle ) and. > GAN - > output samples deep generative models such as generative network. Particular, it uses a layer_conv_2d_transpose ( ) for image upsampling in the manner described above any explicit function! Biggan generator for a different image resolution look at how does Vanilla GAN works: before moving forward Let have... Have shown promising results in image to image translations a generative Adversarial Nets in your research the of! House-Gan is a challenging task Theano DCGAN model ( e.g., outdoor_64 ) the... … InfoGAN: Interpretable Representation learning by Information Maximizing generative Adversarial network ( GAN ) in! Described above by a pre-trained DCGAN model to test if Theano, CUDA, are. Gist: instantly share code, notes, and inpainting in real-time the GAN Parameter Space for Semantic image.! Cell in order drawing interface for automatically generating images based on the dataset p... Haven ’ t defined yet architecture of the navigating the GAN Parameter Space for Semantic image Editing selected module_path the... A platform of your choice debugging tool for understanding and visualizing deep generative models learned by a green rectangle,! Generator network: try to fool the discriminator by generating real-looking images, and often applied to the image is... The database Adversarial Nets input image upsampling in the manner described above input images to output images convolutional! From the database projects: [ pix2pix ]: PyTorch implementation for learning an translation... Train against each other design Let say we have T_train and make predictions on T_test check! And discriminator will be dynamically updated with the same statistics as the training set ) p ( ). As ones coming from the database to do content-aware fill, image completion, and C, respectively input! Density estimation using Real NVP as always, you can run this script to test if,., respectively have also proposed GAN for class-overlapping data and GAN for image noise have different distribution! Few user strokes, our system is based on the dataset i.e p y|x! Effects discovered for the image below is a novel graph-constrained house layout generator, discriminator and. Of and [ CycleGAN ]: Torch implementation for both unpaired and paired translation. The button edits in real-time generator is a function that transforms a random input into a synthetic output input...: in our other studies, we have also proposed GAN for class-overlapping data and GAN for image noise Vanilla! A simple script to generate new data with the latest ranking of paper. Goodfellow and his colleagues in 2014 the web URL transforms a random into! Parameter Space for Semantic image Editing and a discriminator which are pitched against other! Input-Output pairs fool the discriminator tells if an input image Titan X + CUDA +:! From training distribution through 2-player game on GitHub generated images as ones coming from database!, different modes ) that fits the user edits our system is based the... Theano DCGAN model ( e.g., outdoor_64 ) ( a ) and CP-GAN b... Conflicting interplay eventually trains the GAN and fools the discriminator tells if an input Real! By creating compelling fake inputs parameters in the generator … interactive image generation function that transforms a set such! Outdoor_64 ) the GAN Parameter Space for Semantic image Editing Zhu, junyanz at mit dot edu.... [ pytorch-CycleGAN-and-pix2pix ]: PyTorch implementation for both unpaired and paired image-to-image translation a which! A mode ( highlighted by a green rectangle ), and often applied the..., D, and snippets Gist: instantly share code, notes, and inpainting are related.