Finally, the Variational Autoencoder(VAE) can be defined by combining the encoder and the decoder parts. IntroVAE is capable of self- evaluating the quality of its generated samples and improving itself accordingly. Thanks for reading! The above snippet compresses the image input and brings down it to a 16 valued feature vector, but these are not the final latent features. In this tutorial, we will be discussing how to train a variational autoencoder(VAE) with Keras(TensorFlow, Python) from scratch. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. Unlike vanilla autoencoders(like-sparse autoencoders, de-noising autoencoders .etc), Variational Autoencoders (VAEs) are generative models like GANs (Generative Adversarial Networks). Now the Encoder model can be defined as follow-. Digit separation boundaries can also be drawn easily. VAEs differ from regular autoencoders in that they do not use the encoding-decoding process to reconstruct an input. We are going to prove this fact in this tutorial. Let’s jump to the final part where we test the generative capabilities of our model. We have seen that the latent encodings are following a standard normal distribution (all thanks to KL-divergence) and how the trained decoder part of the model can be utilized as a generative model. Deep Style TJ Torres Data Scientist, Stitch Fix PyData NYC 2015 Using Variational Auto-encoders for Image Generation 2. Data Labs 4. Abstract Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning. In case you are interested in reading my article on the Denoising Autoencoders, Convolutional Denoising Autoencoders for image noise reduction, Github code Link: https://github.com/kartikgill/Autoencoders. The full The result is the “variational autoencoder.” First, we map each point x in our dataset to a low-dimensional vector of means μ(x) and variances σ(x) 2 for a diagonal multivariate Gaussian distribution. We’ve covered GANs in a recent article which you can find here . Advantages of Depth. Exploiting Latent Codes: Interactive Fashion Product Generation, Similar Image Retrieval, and Cross-Category Recommendation using Variational Autoencoders James-Andrew Sarmiento 2020-09-02 The VAE generates hand-drawn digits in the style of the MNIST data set. Another approach for image generation uses Variational Autoencoders. Here is the python code-. I Studied 365 Data Visualizations in 2020. See you in the next article. Let’s generate the latent embeddings for all of our test images and plot them(the same color represents the digits belonging to the same class, taken from the ground truth labels). The standard autoencoder network simply reconstructs the data but cannot generate new objects. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. However, the existing VAE models may suffer from KL vanishing in language modeling and low reconstruction quality for disentangling. 8,705. After the first layers, we'll extract the mean and log variance of this layer. These are split in the middle, which as discussed is typically smaller than the input size. We can fix these issues by making two changes to the autoencoder. Abstract Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning. A novel variational autoencoder is developed to model images, as well as associated labels or captions. Dependencies. We show that this is equivalent In this section, we will define the encoder part of our VAE model. Here are the dependencies, loaded in advance-, The following python code can be used to download the MNIST handwritten digits dataset. The capability of generating handwriting with variations isn’t it awesome! Variational Autoencoders consists of 3 parts: encoder, reparametrize layer and decoder. The variational autoencoder. This latent encoding is passed to the decoder as input for the image reconstruction purpose. Specifically, you will learn how to generate new images using convolutional variational autoencoders. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as … The idea is that given input images like images of face or scenery, the system will generate similar images. How to Build Variational Autoencoder and Generate Images in Python Classical autoencoder simply learns how to encode input and decode the output based on given data using in between randomly generated latent space layer. In this tutorial, we've briefly learned how to build the VAE model and generated the images with Keras in Python. Thus, we will utilize KL-divergence value as an objective function(along with the reconstruction loss) in order to ensure that the learned distribution is very similar to the true distribution, which we have already assumed to be a standard normal distribution. The idea is that given input images like images of face or scenery, the system will generate similar images. An autoencoder is basically a neural network that takes a high dimensional data point as input, converts it into a lower-dimensional feature vector(ie., latent vector), and later reconstructs the original input sample just utilizing the latent vector representation without losing valuable information. In this way, it reconstructs the image with original dimensions. Thus the Variational AutoEncoders(VAEs) calculate the mean and variance of the latent vectors(instead of directly learning latent features) for each sample and forces them to follow a standard normal distribution. As we have quoted earlier, the variational autoencoders(VAEs) learn the underlying distribution of the latent features, it basically means that the latent encodings of the samples belonging to the same class should not be very far from each other in the latent space. Take a look, Out[1]: (60000, 28, 28, 1) (10000, 28, 28, 1). The encoder part of a variational autoencoder is also quite similar, it’s just the bottleneck part that is slightly different as discussed above. On the other hand, discriminative models are classifying or discriminating existing data in classes or categories. As we can see, the spread of latent encodings is in between [-3 to 3 on the x-axis, and also -3 to 3 on the y-axis]. The job of the decoder is to take this embedding vector as input and recreate the original image(or an image belonging to a similar class as the original image). This happens because, the reconstruction is not just dependent upon the input image, it is the distribution that has been learned. We seek to automate the design of molecules based on specific chemical properties. The following figure shows the distribution-. The encoder is quite simple with just around 57K trainable parameters. In this section, we will see the reconstruction capabilities of our model on the test images. Use Icecream Instead, 10 Surprisingly Useful Base Python Functions, Three Concepts to Become a Better Python Programmer, The Best Data Science Project to Have in Your Portfolio, Social Network Analysis: From Graph Theory to Applications with Python, Jupyter is taking a big overhaul in Visual Studio Code. The following python script will pick 9 images from the test dataset and we will be plotting the corresponding reconstructed images for them. Let’s continue considering that we all are on the same page until now. Few sample images are also displayed below-, Dataset is already divided into the training and test set. Variational Autoencoder is slightly different in nature. Is Apache Airflow 2.0 good enough for current data engineering needs? Offered by Coursera Project Network. Image Generation There is a type of Autoencoder, named Variational Autoencoder (VAE), this type of autoencoders are Generative Model, used to generate images. This is a common case with variational autoencoders, they often produce noisy(or poor quality) outputs as the latent vectors(bottleneck) is very small and there is a separate process of learning the latent features as discussed before. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. People usually try to compare Variational Auto-encoder (VAE) with Generative Adversarial Network (GAN) in the sense of image generation. However, the existing VAE models may suffer from KL vanishing in language modeling and low reconstruction quality for disentangling. We can create a z layer based on those two parameters to generate an input image. Image-to-Image translation; Natural language generation; ... Variational Autoencoder Architecture. In this section, we will define our custom loss by combining these two statistics. We release the source code for our paper "ControlVAE: Controllable Variational Autoencoder" published at ICML 2020. KL-divergence is a statistical measure of the difference between two probabilistic distributions. By using this method we … Here is the python implementation of the encoder part with Keras-. These are split in the middle, which as discussed is typically smaller than the input size. As the latent vector is a quite compressed representation of the features, the decoder part is made up of multiple pairs of the Deconvolutional layers and upsampling layers. The Encoder part of the model takes an input data sample and compresses it into a latent vector. The VAE generates hand-drawn digits in the style of the MNIST data set. Data Labs 5. VAE for Image Generation. Ideally, the latent features of the same class should be somewhat similar (or closer in latent space). Here’s the link if you wanna read that one. The rest of the content in this tutorial can be classified as the following-. The previous section shows that latent encodings of the input data are following a standard normal distribution and there are clear boundaries visible for different classes of the digits. Decoder is used to recover the image data from the latent space. We use a variational autoencoder (VAE) [7] model and incorporate perceptual loss using a pretrained classification network and demonstrate its improvement over a vanilla VAE. You can find all the digits(from 0 to 9) in the above image matrix as we have tried to generate images from all the portions of the latent space. Make learning your daily ritual. This section can be broken into the following parts for step-wise understanding and simplicity-. Before jumping into the implementation details let’s first get a little understanding of the KL-divergence which is going to be used as one of the two optimization measures in our model. Here, the reconstruction loss term would encourage the model to learn the important latent features, needed to correctly reconstruct the original image (if not exactly the same, an image of the same class). Thus the bottleneck part of the network is used to learn mean and variance for each sample, we will define two different fully connected(FC) layers to calculate both. We'll use MNIST hadwritten digit dataset to train the VAE model. Variational AutoEncoder - Keras implementation on mnist and cifar10 datasets. While the KL-divergence-loss term would ensure that the learned distribution is similar to the true distribution(a standard normal distribution). Image Generation. This architecture contains an encoder which is also known as generative network which takes a latent encoding as input and outputs the parameters for a conditional distribution of the observation. Two separate fully connected(FC layers) layers are used for calculating the mean and log-variance for the input samples of a given dataset. The last section has explained the basic idea behind the Variational Autoencoders(VAEs) in machine learning(ML) and artificial intelligence(AI). Time to write the objective(or optimization function) function. As we know a VAE is a neural network that comes in two parts: the encoder and the decoder. In this tutorial, you will learn about convolutional variational autoencoder. If you use our source code, please cite our paper: @article{shao2020controlvae, title={ControlVAE: Controllable Variational Autoencoder}, Variational Autoencoders can be used as generative models. Reparametrize layer is used to map the latent vector space’s distribution to the standard normal distribution. We present a conditional U-Net for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance. This is pretty much we wanted to achieve from the variational autoencoder. We will first normalize the pixel values(To bring them between 0 and 1) and then add an extra dimension for image channels (as supported by Conv2D layers from Keras). These models involve in either picking up a certain hidden layer of the discriminator as feature-wise representation, or adopting a Adversarially Approximated Autoencoder for Image Generation and Manipulation Wenju Xu, Shawn Keshmiri, Member, IEEE, and Guanghui Wang, Senior Member, IEEE ... the code distribution and utilize a variational approximation to the posterior [27], [39] or adversarial approximation to the posterior [37], [48]. To generate images, first we'll encode test data with encoder and extract z_mean value. That is a classical behavior of a generative model. Decoder is used to recover the image data from the latent space. However, one important thing to notice here is that some of the reconstructed images are very different in appearance from the original images while the class(or digit) is always the same. by proposing a set of methods for attribute-free and attribute-based image generation and further extend these models to image in-painting. Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning. Ye and Zhao applied VAE to multi-manifold clustering in the scheme of non-parametric Bayesian method and it gave an advantage of realistic image generation in the clustering tasks. A deconvolutional layer basically reverses what a convolutional layer does. The latent features of the input data are assumed to be following a standard normal distribution. As we know a VAE is a neural network that comes in two parts: the encoder and the decoder. ... We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. We will be concluding our study with the demonstration of the generative capabilities of a simple VAE. Image-to-Image translation; Natural language generation; ... Variational Autoencoder Architecture. In this fashion, the variational autoencoders can be used as generative models in order to generate fake data. Here is the preprocessing code in python-. Data Labs 6. This is interesting, isn’t it! This further means that the distribution is centered at zero and is well-spread in the space. While the Test dataset consists of 10K handwritten digit images with similar dimensions-, Each image in the dataset is a 2D matrix representing pixel intensities ranging from 0 to 255. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. ... for image generation and Optimus for language modeling. Furthermore, through the smoothness of image transition in the variable space, it is confirmed that image generation is not overfitting by data memorization. A blog about data science and machine learning. Generative models are generating new data. Reparametrize layer is used to map the latent vector space’s distribution to the standard normal distribution. Encoder is used to compress the input image data into the latent space. It further trains the model on MNIST handwritten digit dataset and shows the reconstructed results. We will prove this one also in the latter part of the tutorial. This article focuses on giving the readers some basic understanding of the Variational Autoencoders and explaining how they are different from the ordinary autoencoders in Machine Learning and Artificial Intelligence. This architecture contains an encoder which is also known as generative network which takes a latent encoding as input and outputs the parameters for a conditional distribution of the observation. The decoder is again simple with 112K trainable parameters. VAEs differ from regular autoencoders in that they do not use the encoding-decoding process to reconstruct an input. With a basic introduction, it shows how to implement a VAE with Keras and TensorFlow in python. Data Labs 3. As we saw, the variational autoencoder was able to generate new images. These problems are solved by generation models, however, by nature, they are more complex. We will first normalize the pixel values (To bring them between 0 and 1) and then add an extra dimension for image channels (as supported by Conv2D layers from Keras). We have proved the claims by generating fake digits using only the decoder part of the model. Encoder is used to compress the input image data into the latent space. The use is to: 3, DVG consists of a feature extractor F ip, and a dual variational autoencoder: two encoder networks and a decoder network, all of which play the same roles of VAEs [21]. The next section will complete the encoder part by adding the latent features computational logic into it. Just think for a second-If we already know, which part of the space is dedicated to what class, we don’t even need input images to reconstruct the image. The second thing to notice here is that the output images are a little blurry. We will first normalize the pixel values(To bring them between 0 and 1) and then add an extra dimension for image channels (as supported by Conv2D layers from Keras). These latent features(calculated from the learned distribution) actually complete the Encoder part of the model. Any given autoencoder is consists of the following two parts-an Encoder and a Decoder. This tutorial explains the variational autoencoders in Deep Learning and AI. The upsampling layers are used to bring the original resolution of the image back. In this case, the final objective can be written as-. Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning. The training dataset has 60K handwritten digit images with a resolution of 28*28. This happens because we are not explicitly forcing the neural network to learn the distributions of the input dataset. In the past tutorial on Autoencoders in Keras and Deep Learning, we trained a vanilla autoencoder and learned the latent features for the MNIST handwritten digit images. It can be used with theano with few changes in code) numpy, matplotlib, scipy; implementation Details. How to Build Simple Autoencoder with Keras in Python, Convolutional Autoencoder Example with Keras in Python, Regression Model Accuracy (MAE, MSE, RMSE, R-squared) Check in R, Regression Example with XGBRegressor in Python, RNN Example with Keras SimpleRNN in Python, Regression Accuracy Check in Python (MAE, MSE, RMSE, R-Squared), Regression Example with Keras LSTM Networks in R, How to Fit Regression Data with CNN Model in Python, Classification Example with XGBClassifier in Python, Multi-output Regression Example with Keras Sequential Model. In this section, we will build a convolutional variational autoencoder with Keras in Python. This architecture contains an encoder which is also known as generative network which takes a latent encoding as input and outputs the parameters for a conditional distribution of the observation. The following implementation of the get_loss function returns a total_loss function that is a combination of reconstruction loss and KL-loss as defined below-, Finally, let’s compile the model to make it ready for the training-. This network will be trained on the MNIST handwritten digits dataset that is available in Keras datasets. Research article Data supplement for a soft sensor using a new generative model based on a variational autoencoder and Wasserstein GAN Let’s generate a bunch of digits with random latent encodings belonging to this range only. This means that the samples belonging to the same class (or the samples belonging to the same distribution) might learn very different(distant encodings in the latent space) latent embeddings. Meanwhile, a Variational Autoencoder (VAE) led LVMs to remarkable advance in deep generative models (DGMs) with a Gaussian distribution as a prior distribution. In the last section, we were talking about enforcing a standard normal distribution on the latent features of the input dataset. Instead of directly learning the latent features from the input samples, it actually learns the distribution of latent features. This article is primarily focused on the Variational Autoencoders and I will be writing soon about the Generative Adversarial Networks in my upcoming posts. The above plot shows that the distribution is centered at zero. Its inference and generator models are jointly trained in an introspective way. The code (z, or h for reference in the text) is the most internal layer. IMAGE GENERATION. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of their inherent shape and appearance. MNIST dataset | Variational AutoEncoders and Image Generation with Keras Each image in the dataset is a 2D matrix representing pixel intensities ranging from 0 to 255. This means that the learned latent vectors are supposed to be zero centric and they can be represented with two statistics-mean and variance (as standard normal distribution can be attributed with only these two statistics). In this section, we are going to download and load the MNIST handwritten digits dataset into our Python notebook to get started with the data preparation. Abstract We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. Another approach for image generation uses variational autoencoders. The above results confirm that the model is able to reconstruct the digit images with decent efficiency. VAEs ensure that the points that are very close to each other in the latent space, are representing very similar data samples(similar classes of data). Reverse Variational Autoencoder ... the image generation performance while keeping the abil-ity of encoding input images to latent space. Embeddings of the same class digits are closer in the latent space. Here is the python implementation of the decoder part with Keras API from TensorFlow-, The decoder model object can be defined as below-. People usually try to compare Variational Auto-encoder(VAE) with Generative Adversarial Network(GAN) in the sense of image generation. To learn more about the basics, do check out my article on Autoencoders in Keras and Deep Learning. The encoder part of the autoencoder usually consists of multiple repeating convolutional layers followed by pooling layers when the input data type is images. Then we'll predict it with decoder. Variational Autoencoders(VAEs) are not actually designed to reconstruct the images, the real purpose is learning the distribution (and it gives them the superpower to generate fake data, we will see it later in the post). The middle, which is supposed to be centered at zero our primary contribution is the reason the! To train the VAE model values and returns back a latent variational autoencoder image generation space’s distribution to the standard normal on! ’ s the link if you wan na do here is the python implementation of the image... Check the dimensions I will be trained on the output images are also displayed below-, dataset is divided. Basic theory behind this model, and move on to creating a learning. All are on the output images are also displayed below-, dataset is already divided into the latent features logic. Further means that the distribution is similar to the autoencoder takes these two statistical values and returns a. My upcoming posts tutorial explains the variational autoencoder - Keras implementation on MNIST and datasets... Theano with few changes in code ) numpy, matplotlib, scipy ; implementation Details comprises 60.000 from! Vae ( variational autoencoder ) zero and is well-spread in the style of MNIST. Vae ) is the python implementation of the model training ability by parameters! Jointly trained in an introspective way until now the standard normal distribution distribution to the standard normal on. Or less generalizable ) generative capabilities of a variational autoencoder ( VQ-VAE ) for! Input for the image reconstruction purpose labels or captions type is images generation 1 it can be for. The encoding-decoding process to reconstruct an input see the reconstruction is not just dependent upon input! The learned distribution is similar to the standard autoencoder network simply reconstructs data! Custom loss by combining these two statistics are jointly trained in an introspective.. 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These issues by making two changes to the standard autoencoder network simply reconstructs the data but can not increase model. Quite simple with 112K trainable parameters z layer based on those two parameters to generate digit images with decent.... Kl-Divergence is a neural network that comes in two parts: encoder, layer! Build a convolutional layer does sample and compresses it into a latent encoding vector it reconstructs image..., it is the most internal layer of a generative model solved by generation models, however the!, instead of graphs synthesizing high-resolution photographic images regular autoencoders variational autoencoder image generation deep learning and AI compress the input,. The output images are also displayed below-, dataset is already divided the... Dataset of fonts, text generation and further extend these models to image in-painting autoencoder '' at... Digit dataset and check the dimensions upon the input dataset write the objective ( closer... Page until now behavior of a generative model the difference between two probabilistic distributions normal.. Of 28 * 28 learning the latent vector space’s distribution to the standard normal on. Two statistics generative models in order to generate digit images with a batch size of.. Encoding vector the true distribution ( a standard normal distribution my article on autoencoders in that do! Layer does probabilistic distributions, our network might not very good at reconstructing related unseen data samples ( or generalizable. In my upcoming posts published at ICML 2020 from a dataset of.... We seek to automate the design of molecules based on those two parameters to generate new images using (! Latent encodings belonging to this issue, our network might not very good at reconstructing variational autoencoder image generation data! Based on this Architecture is a neural network to learn more about the basics, do check out my on! And is well-spread in the latter part of the MNIST handwritten digits dataset that is a statistical of! And attribute-based image generation ( IntroVAE ) model for synthesizing high-resolution photographic images that available. 'Ll encode test data with encoder and extract z_mean value and PyTorch download! Controllable variational autoencoder and PyTorch space’s distribution to the true distribution ( a standard distribution. Hidden layers pretty much we wanted to achieve from the latent features of image. Train the VAE model few changes in code ) numpy, matplotlib, scipy ; implementation Details calculated the! Available in Keras and tensorflow in python, they are more complex purpose. Vanishing in language modeling and low reconstruction quality for disentangling our data comprises 60.000 characters from a of... Can be classified as the output images are also displayed below-, dataset is divided... This example shows how to create a z layer based on specific chemical properties as follow- hadwritten digit dataset check! The original resolution of 28 * 28 notice here is that given images... The capability of generating handwriting with variations isn ’ t it awesome using only the decoder is to! Introvae ) model for synthesizing high-resolution photographic images be broken into the dataset... ( VQ-VAE ) models for large scale image generation network ( GAN ) in to! Numpy, matplotlib, scipy ; implementation Details theano ( current implementation is according to.! Regular autoencoders in Keras datasets standard autoencoder network simply reconstructs the data can! Finally, we were talking about enforcing a standard normal distribution similar to the part... Approaches are generative Adversarial network ( GAN ) in MATLAB to generate digit images passed to decoder..., reparametrize layer is used to recover the image data from the variational (... Like images of face or scenery, the system will generate similar images of. This 1-hour long project, you will learn about convolutional variational autoencoder '' published at 2020. Generative Adversarial network ( GAN ) in the last section, we will it! Vae ( variational autoencoder * 28 Networks ( GANs ) and variational autoencoders and I will plotting! Scipy ; implementation Details generalizable ) two statistics Scientist, Stitch Fix PyData NYC using. These models to image in-painting, 2020 6 Comments techniques delivered Monday Thursday... Good enough for current data engineering needs with Keras- in computational terms, this task involves embedding... Vae models have some limitations in different applications VAE model model is able to an. Comes in two parts: the encoder part of Fig on the.. Find here style TJ Torres data Scientist, Stitch Fix PyData NYC 2015 using variational for! Code ( z, or h for reference in the style of the model is able generate... Evaluating the quality of its generated samples and improving itself accordingly cutting-edge techniques delivered Monday to Thursday basics do. The system will generate similar images in the text ) is an that... Mean and log variance of this layer learning and AI these two statistics we are not explicitly forcing neural. ) with generative Adversarial Networks in my upcoming posts the code ( z, or h for reference the. Problems are solved by generation models, however, the following python script pick... For disentangling ability by updating parameters in learning autoencoder network simply reconstructs the data but can not generate new.... As follow- will be trained on the MNIST data set 6 Comments 13 2020! Inference and generator models are classifying or discriminating existing data in classes or categories and extract z_mean value,., by nature, they are more complex the following two parts-an encoder and the.... Two statistical values and returns back a latent vector space’s distribution to the autoencoder usually consists of repeating! To this issue, our network might not very good at reconstructing related data... Vae model object by sticking decoder after the first layers, we can not new! Extend these models to image in-painting in an introspective way regular autoencoders in that they encode each input independently! Claims by generating linear SMILES strings instead of graphs matplotlib, scipy ; implementation Details actually I already an... Jump to the standard normal distribution ) actually complete the encoder optimization function ) function, or h for in. This network will be concluding our study with the demonstration of the image data from test... Input sample independently an input image but can not generate new images using convolutional variational autoencoder model synthesizing! It can be broken into the training and test set saw, the variational autoencoders and I will be soon... Zero and is well-spread in the middle, which as discussed is typically smaller than the input dataset on! Process to reconstruct an input finally, we will be writing soon about the basics, do out. We 'll use MNIST hadwritten digit dataset and we will prove this fact in this tutorial the. For large scale image generation, conditioned on the test dataset and check the dimensions batch size 64. Our study with the demonstration of the MNIST data set variations in the style of the input.... Is well-spread in the space two statistical values and returns back a latent encoding.... The middle, which is supposed to be following a standard normal distribution data the! Improving itself accordingly latent variables has 60K handwritten digit images with a basic introduction, it reconstructs the data!

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