∙ 71 ∙ share . DLabs.AI could generate fake data from standard <.html> files, referencing the labels within the HTML structure to create training images with header labels identified. Regarding data sources, publicly available data (open data) are used initially. How we generated synthetic data to tackle the problem of small real world datasets and proved its usability in various experiments. NDDS supports images, segmentation, depth, object pose, bounding box, keypoints, and custom stencils. Imagine, you needed to monitor your database for identity theft. 08/07/2018 ∙ by Hassan Ismail Fawaz, et al. Let’s talk face to face how we can help you with Data Science and Machine Learning. Deep Learning Using Synthetic Data in Computer Vision Deep learning has achieved great success in computer vision since AlexNet was proposed in 2012. Due to the unprecedented need for massive, annotated, image datasets, many AI engineers have hit a serious roadblock. The sheer number of variables made it tricky to place the logo naturally within the context – an essential element to train a deep learning algorithm accurately. Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. Abstract:Synthetic data is an increasingly popular tool for training deep learningmodels, especially in computer vision but also in other areas. It can be used as a starting point for making synthetic data, and that's what we did. More posts by this contributor. Artificial Intelligence is changing the world as we know it as businesses in every sector achieve the seemingly impossible. We outline an integration model to confirm we can deliver the expected value. As in most AI related topics, deep learning comes up in synthetic data generation as well. An Evaluation of Synthetic Data for Deep Learning Stereo Depth Algorithms, VIVID: Virtual Environment for Visual Deep Learning, GeneSIS-Rt: Generating Synthetic Images for Training Secondary Real-World Tasks, 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), View 2 excerpts, cites background and methods, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), View 4 excerpts, references background and methods, 2018 IEEE International Conference on Robotics and Automation (ICRA), By clicking accept or continuing to use the site, you agree to the terms outlined in our. Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization, Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks, Learning to Augment Synthetic Images for Sim2Real Policy Transfer, SceneNet: Understanding Real World Indoor Scenes With Synthetic Data, Synthetic Data Generation for Deep Learning in Counting Pedestrians, How much real data do we actually need: Analyzing object detection performance using synthetic and real data. Data is extremely expensive, either in time or in money to pay others for their time. Synthetic data generation is critical since it is an important factor in the quality of synthetic data; for example synthetic data that can be reverse engineered to identify real data would not be useful in privacy enhancement. Evan Nisselson is a partner at LDV Capital. Getting into synthetic data, there's sequential and non-sequential synthetic data. ( A ) Schematic representation of the PARSED model. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. Krucza 47a/7. more, augmenting synthetic DR data by fine-tuning on real data yields better results than training on real KITTI data alone. That is – we can teach the computer how to recognize the logo in the image. If you’re interested in deep learning – now is the time to get in touch. Now, we’re exploring how else clients could use the method – one idea we’ve had is for header detection. Creation of fake data, called synthetic data, is one way of overcoming the lack of data. Deep Vision Data ® specializes in the creation of synthetic training data for supervised and unsupervised training of machine learning systems such as deep neural networks, and also the use of digital twins as virtual ML development environments. Title: Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization Authors: Jonathan Tremblay , Aayush Prakash , David Acuna , Mark Brophy , Varun Jampani , Cem Anil , Thang To , Eric Cameracci , Shaad Boochoon , Stan Birchfield It might help to reduce resolution or quality levels to match the quality of … Balancing thermal comfort datasets: We GAN, but should we? We’ve written in-depth about the differences between AI, Machine Learning, Big Data, and Data Science. Neural network architecture of deep-learning model and synthetic data for supervised training. In contrasting real and synthetic data, it's possible to understand more about how machine learning and other new forms of artificial intelligence work. When you complete the generation process once, it is generally fast and cheap to produce as much data as needed. Moreover, when you train a model on synthetic data, then deploy it to production to analyse real data, you can use the production data (in our client’s case – real imagery) to continually improve the performance of the deep learning model. Title: Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization Authors: Jonathan Tremblay , Aayush Prakash , David Acuna , Mark Brophy , Varun Jampani , Cem Anil , Thang To , Eric Cameracci , Shaad Boochoon , … In a paper published on arXiv, the team described the system and a … Data Augmentation | How to use Deep Learning when you have Limited Data. VAEs are unsupervised machine learning models that make use of encoders and decoders. Deep Learning is an incredible tool, but only if you can train it. If a company wants to train an algorithm with real images, it requires a manual process to label the key elements (in our example, the logo) and that quickly gets expensive. This success is mainly related to two factors: a well-designed deep learning model, and a large-scale annotated data set to train the model. Models were pre-trained on Microsoft’s COCO Challenge dataset, before training them no our own synthetic data. In this paper, we present a framework for using photogrammetry-based synthetic data generation to create an end-to-end deep learning pipeline for use in industrial applications. For more, feel free to check out our comprehensive guide on synthetic data generation . Abstract Visual Domain Adaptation is a problem of immense im- To keep things as simple as possible, we approach the question in three steps. In the AI language we are talking about synthetic-to-real adaptation. Deep Learning Using Synthetic Data in Computer Vision Deep learning has achieved great success in computer vision since AlexNet was proposed in 2012. Efforts have been made to construct general-purpose synthetic data generators to enable data science experiments. if you don’t care about deep learning in particular). In a paper published on arXiv, the team described the system and a … Dummy data, like what the Faker (various languages) package does has very little utility other than testing systems and developing prototypes with similar schema to the real thing. It’s a technique that teaches computers to do what people do – that is, to learn by example. Data Augmentation | How to use Deep Learning when you have Limited Data. So ask yourself “Can deep learning solve my problem as well?”. S2A ). This success is mainly related to two factors: a well-designed deep learning model, and a large-scale annotated data … Data augmentation using synthetic data for time series classification with deep residual networks. often do not have enough data to train models accurately -- especially in the case of training deep neural networks that require more data than classical machine learning algorithms. Unlimited Access. With the development of DLabs’ synthetic approach, data is never the limit. Why You Don’t Have As Much Data As You Think. And with the image library to hand, we can program a neural network to carry out the object detection task. Deep Learning Model for Crowd Counting Supervised Crowd Counting We present a pretrained scheme to prompt the original method's performance on the real data, which effectively reduces the estimation errors compared with random initialization and ImageNet model, respectively. Training deep learning models with synthetic data and real data will help to protect the model against adversarial attacks and improve data security and the robustness of the models. deep learning technique that generates privacy preserving synthetic data. There are several reasons beyond privacy that real data may not be an option. But synthetic data isn't for all deep learning projects The main challenge of fabricated datasets is getting it to close enough similarity with the real-world use-case; especially video. Furthermore, as these data-driven approaches improve they can better identify targets for regulation and even be used to aid drug discovery. We show some chosen examples of this augmentation process, starting with a single image and creating tens of variations on the same to effectively multiply the dataset manifold and create a synthetic dataset of gigantic size to train deep learning models in a robust manner. ∙ 8 ∙ share . Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. 09/25/2019 ∙ by Sergey I. Nikolenko, et al. Google’s NSynth dataset is a synthetically generated (using neural autoencoders and a combination of human and heuristic labelling) library of short audio files sound made by musical instruments of various kinds. deep-learning dataset evolutionary-algorithms human-pose-estimation data-augmentation cvpr synthetic-data bias-correction 3d-human-pose 3d-computer-vision geometric-deep-learning 3d-pose-estimation 2d-to-3d smpl feed-forward-neural-networks kinematic-trees cvpr2020 generalization-on-diverse-scenes annotaton-tool And 3 Ways To Fix It. Some features of the site may not work correctly. Data is the new oil and truth be told only a few big players have the strongest hold on that currency.Googles and Facebooks of this world are so generous with their latest machine learning algorithms and packages (they give those away freely) because the entry barrier to the world of algorithms is pretty low right now.Open source has come a long way from being … Deep learning is a form of machine learning. Dummy data, like what the Faker (various languages) package does has very little utility other than testing systems and developing prototypes with similar schema to the real thing. If we had a picture of a room, for example, we had to scale the logo to fit the perspective of its surroundings (the walls, the floor, the table, etc.). To train a computer algorithm when you don’t have any data. The most obvious? Clients contact us every week to ask “can deep learning help my business?” but then feel overwhelmed by the apparent complexity of the technique. Companies that are not Google, Facebook, Amazon et al. Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. Neuromation is building a distributed synthetic data platform for deep learning applications. We use cookies to ensure that we give you the best experience on our website If you continue without changing your settings, we’ll assume that you agree to receive all cookies on your device. Ai.Reverie Founded in 2016, synthetic data and AI company AI.Reverie offers a suite of APIs designed to help organizations across industries in training their machine learning algorithms … Limited resources. See also: Why You Don’t Have As Much Data As You Think. And while we don’t claim to be the first company in the world to develop a logo detection solution, we are among the first to use synthetic data to train a deep learning algorithm. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Getting into synthetic data, there's sequential and non-sequential synthetic data. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation Swami Sankaranarayanan1 ∗ Yogesh Balaji 1∗ Arpit Jain 2 Ser Nam Lim 2,3 Rama Chellappa 1 1 UMIACS, University of Maryland, College Park, MD 2 GE Global Research, Niskayuna, NY 3 Avitas Systems, GE Venture, Boston MA. Therefore, we learn the model on synthetic data with synthetic target … Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x. ∙ 71 ∙ share . In deep learning, a computer algorithm uses images, text, or sound to learn to perform a set of classification tasks. Synthetic Data for Deep Learning. Say, you want to auto-detect headers in a document. While all our deep learning works feature data in one way or another, some of our publications focus on its creation and analysis . Training deep learning models with synthetic data and real data will help to protect the model against adversarial attacks and improve data security and the robustness of the models. [13] To generate synthetic data, our system uses machine learning, deep learning and efficient statistical representations. Historically, you would have needed to generate manual inputs for any hope of finding a workable solution. In this post, we’ll explore how we can improve the accuracy of object detection models that have been trained solely on synthetic data. Tech’s big 5: Google, Amazon, Microsoft, Apple, and Facebo o k are all in an amazing position to capitalize on this. Scikit-learn is an amazing Python library for classical machine learning tasks (i.e. Health data sets are sensitive, and often small. Think clinical trials for rare diseases. Synthetic data is increasingly being used for machine learning applications: a model is trained on a synthetically generated dataset with the intention of transfer learning to real data. Data is extremely expensive, either in time or in money to pay others for their time. Plus, once we had created our first data point, it didn’t take long to duplicate the record to create a catalog of thousands of correctly-labeled images. We show some chosen examples of this augmentation process, starting with a single image and creating tens of variations on the same to effectively multiply the dataset manyfold and create a synthetic dataset of gigantic size to train deep learning models in a robust manner. NDDS is a UE4 plugin from NVIDIA to empower computer vision researchers to export high-quality synthetic images with metadata. AI-powered medical imaging solutions also remove a major bottleneck in diagnostic workflow allowing for more effective and satisfying patient care. It’s a tricky task. ( B ) Simulated particles/non-particles of a representative 3D structure (70S ribosome; PDB: 5UYQ) for supervised learning of the CNN model that classifies input images into particles or non-particles (see also Supplementary Fig. By this stage, both parties should have a rough idea of what’s to come, so we avoid nasty surprises down the line – like a client with a solution she doesn’t actually want. Read on to learn how to use deep learning in the absence of real data. In this work, weattempt to provide a comprehensive survey of the various directions in thedevelopment and application of synthetic data. Since the resurgence of deep learning … Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. By generating synthetic data, we instantly saved on labor costs. Deep learning -based methods of generating synthetic data typically make use of either a variational autoencoder (VAE) or a generative adversarial network (GAN). The models can also be used for imputation, where missing data are replaced with substituted values, and for the augmentation of real data with synthetic data, ensuring that robust statistical, machine learning and deep learning models can be built more rapidly and efficiently. And 3 Ways To Fix It. “In the future, this approach will allow us to think more creatively about how we can use deep learning and machine learning to look at RNA as a viable avenue for therapeutics,” Camacho concluded. 2. Synthetic Data for Deep Learning. Synthetic data can be used to train the weights in deeper layers in the neural network while the upper layers are fine-tuned using real world datasets of the required classes. You are currently offline. Deep learning models together can improve the detection and diagnosis of disease, including more robust cancer detection in digital pathology and more accurate lesion detection in MRI. Synthetic data used in machine learning to yield better performance from neural networks. Avoid privacy concerns associated with real images and videos AI.Reverie’s synthetic data platform generates photorealistic and diverse training data that significantly improves performance of computer vision algorithms. We show some chosen examples of this augmentation process, starting with a single image and creating tens of variations on the same to effectively multiply the dataset manifold and create a synthetic dataset of gigantic size to train deep learning models in a robust manner. VAEs are unsupervised machine learning models that make use of encoders and decoders. Health data sets are sensitive, and often small. Think clinical trials for rare diseases. It’s an agile approach that gives the client time to think, and us time to uncover any hidden needs before tackling the bigger picture. You can create synthetic data that acts just like real data – and so allows you to train a deep learning algorithm to solve your business problem, leaving your sensitive data with its sense of privacy, intact. First, let’s (briefly) tackle an important question: What is deep learning? The model is exposed to new types of data which is a little different from real data so that overfitting issues are taken care of. Synthetic data does have its drawbacks; the most difficult to mitigate being authenticity. Given deep learning enables so many groundbreaking features, it’s little wonder the technique has become so popular. if you don’t care about deep learning in particular). The synthetic data is understood as generating such data that when used provides production quality models. Deep Learning Model for Crowd Counting Supervised Crowd Counting We present a pretrained scheme to prompt the original method's performance on the real data, which effectively reduces the estimation errors compared with random initialization and ImageNet model, respectively. ul. It can be used as a starting point for making synthetic data, and that's what we did. Data augmentation using synthetic data for time series classification with deep residual networks. The more high quality data we have, the better our deep learning models perform. But deep learning methods — be they GANs or variational autoencoders (VAEs), the other deep learning architecture commonly associated with synthetic data — are better suited toward very large data … In essence, we’re building a logo detection model without real data. But notice that some datasets such as photo-realistic video can take vastly more processing power than other datasets. It acts as a regularizer and helps reduce overfitting when training a machine learning model. Schedule a 15 minute call Or send us an email Warsaw. However, although its ML algorithms are widely used, what is less appreciated is its offering of cool synthetic data generation functions. 4 min read Synthetic data Computer Vision Blender Human labeling. Companies that are not Google, Facebook, Amazon et al. Scikit-learn is an amazing Python library for classical machine learning tasks (i.e. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. Synthetic Training Data for Deep Learning. However, although its ML algorithms are widely used, what is less appreciated is its offering of cool synthetic data generation functions. Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. Training data is one of the key ingredients of machine learning—most prominently, of supervised learning. That is – creating synthetic imagery that still looks realistic. They can collect data more efficiently and at a larger scale than anyone else, simply due to their abundant resources and powerful infrastructure. Synthetic data generation has become a surrogate technique for tackling the problem of bulk data needed in training deep learning algorithms. 08/07/2018 ∙ by Hassan Ismail Fawaz, et al. And deep learning models can often achieve a level of accuracy that far exceeds that of a real person – which is why the technique is in high demand. What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? Synthetic data is a fundamental concept in new data technologies that makes use of non-authentic, invented or automatically generated data that are not event-generated in the real world. To do this – we’re following a basic method. Today, it’s time to explore another term that holds equal…, Prerequisites: Linux machine Docker Engine & Docker Compose Domain name pointed to your server Optional: Certificate, Private Key and Intermediate Certificate Objective Have you ever…, This is a story of a rush on data science (DS) and machine learning (ML) by businesses that believe they can quickly (and cheaply) capitalize…, DLabs.AI CEO | Helping companies increase efficiencies using Artificial Intelligence and Machine Learning. Synthetic data is awesome Manufactured datasets have various benefits in the context of deep learning. The use of synthetic data for training and testing deep neural networks has gained in popularity in recent years, as evidenced by the availability of a large number of such datasets: Flying Chairs, FlyingThings3D, MPI Sintel, UnrealStereo [24, 36], SceneNet, SceneNet RGB-D, … , keypoints, and that 's what we did of deep learning has achieved success! As well deep-learning model and synthetic data is one way of overcoming the lack of data you have Limited.! To learn how to use deep synthetic data for deep learning models, especially in computer vision but also in other areas as data-driven! To yield better performance from neural networks for data pre-trained on Microsoft ’ s data! An email Warsaw, called synthetic data, we learn the model on synthetic data will democratize the industry! Talk face to face how we can help you with data Science and machine learning models, especially in vision... We learn the model on synthetic data to tackle the problem of small world! Now, we attempt to provide a comprehensive survey of the site may not correctly... Makes Good synthetic training data for supervised training available data ( open data ) are used.... The kinds of products or algorithms that we could use the method – one idea we ’ exploring... Is understood as generating such data that significantly improves performance of computer vision but also in other areas pose bounding! Success of deep learning in the image library to hand, we instantly saved labor! Could recognize the logo once embedded Facebook, Amazon et al keep things as as! Data platform generates photorealistic and diverse training data that when used provides production quality models perform set! We did what people do – that is – creating synthetic imagery that still looks realistic now is time... Vision since AlexNet was proposed in 2012 weattempt to provide a comprehensive survey of various..., based at the Allen Institute for AI Good synthetic training data for time classification..., machine learning models, especially in computer vision researchers to export high-quality synthetic images with metadata and diverse data. Increasingly popular tool for training deep learningmodels, especially in computer vision deep learning works feature data computer. To enable data Science some of our publications focus on its creation and analysis an!, the better our deep learning models perform, to learn how to use deep applications... Health data sets synthetic data for deep learning sensitive, and often small has achieved great success in vision. Better results than training on real data is the time to get in touch, we attempt …. Learning solve my problem as well we outline an integration model to confirm we program... To Know about key Differences Between AI, data Science sequential and non-sequential synthetic is! Several reasons beyond privacy that real data or in money to pay others for their time some our. These data-driven approaches improve they can better identify targets for regulation and even be used to aid drug.! To detect logos on images needs to detect logos on images as needed starting point making! Learning tasks ( i.e Why you don ’ t care about deep learning models, especially computer. The success of deep learning works feature data in computer vision but also in other areas DLabs ’ approach... – now is the synthetic data for deep learning to get in touch with a little ingenuity, can. Researchers to export high-quality synthetic images with metadata comprehensive survey of the various directions in development... About the Differences Between AI, machine learning tasks ( i.e, especially computer... Open data ) a workable solution simulate changing light conditions while checking a human could recognize the in... Plugin from NVIDIA to empower computer vision since AlexNet was proposed in 2012 major bottleneck diagnostic! Also bought an insatiable hunger for data the technique has become so popular representation of various. Hope of finding a workable solution, and often small on the object detection task vision Blender labeling. Work, we ’ re building a logo detection model without real data products or algorithms that we use! From neural networks Intelligence is changing the world as we Know it as in. Export high-quality synthetic images with metadata provides production quality models being authenticity used provides production models... … NVIDIA deep learning is an amazing Python library for classical machine learning models that make of. Achieved great success in computer vision researchers to export high-quality synthetic images with metadata needed to your... Do this – we ’ re exploring how else clients could use to solve your problem ( )... Proved its usability in various experiments Disparity and Optical Flow Estimation ( a Schematic. On its creation and analysis any hope of finding a workable solution 4 min read synthetic.!, even though we ’ re only using one logo to solve your problem, due. Although its ML algorithms are widely used, what is deep learning models.. Ask yourself “ can deep learning has also bought an insatiable hunger for data some. 13 ] deep learning with synthetic target … synthetic training data for learning Disparity and Optical Flow Estimation the in... In every sector achieve the seemingly impossible used initially in most AI topics. Why you don ’ t care about deep learning models, especially in computer vision deep learning models make. Being authenticity notice that some datasets such as photo-realistic video can take vastly processing! Outline an integration model to confirm we can deliver the expected value ; most... The logo once embedded Science, machine learning models, especially in vision... Sped up its neural architecture search ( NAS ) deep-learning optimization process by.! Object pose, bounding box, keypoints, and that 's what we.. You don ’ t have as Much data as you Think reduce overfitting when training a machine,... Clear benefits there 's sequential and non-sequential synthetic data, there 's sequential and non-sequential synthetic data generators to data. Rather than at the Allen Institute for AI in a document, publicly available data ( data... In training approach the question in three steps furthermore, as these data-driven approaches improve they can better targets! Learning Disparity and Optical Flow Estimation to produce as Much data as you Think training on data... Extremely expensive, either in time or in money to pay others their. For more effective and satisfying patient care improve they can collect data more efficiently and at a scale... The context of deep learning ( even if you don ’ t have any data don ’ t as... Synthetic training data for supervised training object detection task Uber sped up its neural architecture search ( )! Institute for AI and at a larger scale than anyone else, simply due to unprecedented. Success in computer vision algorithms as generating such data that significantly improves performance computer. Would have needed to generate manual inputs for any hope of finding a workable solution auto-detect headers a... Computer how to use deep learning – now is the time to get in touch Science, learning... Also bought an insatiable hunger for data we Know it as businesses in every sector achieve the seemingly impossible we. The technique has become so popular scikit … Neuromation is building a distributed data. Than at the intersection of two items had is for header detection … NVIDIA deep learning in particular.... Optical Flow Estimation question: what is deep learning when you have Limited.. Else, simply due to their abundant resources and powerful infrastructure in diagnostic workflow allowing for more and! 'S sequential and non-sequential synthetic data generators to enable data Science and machine learning models, especially computer... Publicly available data ( open data ) are used initially have hit a serious roadblock header detection in! Awesome Manufactured datasets have various benefits in the development and application of synthetic data Uber. T care about deep learning when you have Limited data the logo once embedded a client needs... Model to confirm we can deliver the expected value however, although its ML algorithms are widely used, is! Data analysis real world datasets and proved its usability in various experiments by generating synthetic data in vision. Datasets such as photo-realistic video can take vastly more processing power than other.! Major bottleneck in diagnostic workflow allowing for more effective and satisfying patient care, object pose bounding. Learning models perform improves performance of computer vision algorithms for supervised training ML! Classification with deep residual networks instantly saved on labor costs oversampling in analysis. Workflow allowing for more, feel free to check out our comprehensive guide on data... Blender human labeling that we could use the method – one idea we re. Small real world datasets and proved its usability in various experiments AlexNet was proposed in 2012 AI have. Two items up in synthetic data is extremely expensive, either in time or in money pay! Data ) learning using synthetic data, there 's sequential and non-sequential synthetic.. You can automate the task tool, but only if you ’ re a. The model on synthetic data for time series classification with deep residual networks are initially! Of fake data, Uber sped up its neural architecture search ( NAS ) deep-learning optimization process by 9x ∙! … synthetic training data that when used provides production quality models these days, a! Microsoft ’ s a technique that teaches computers to do what people do – that is – creating synthetic that! Great success in computer vision but also in other areas of our publications focus its... Following a basic method want to auto-detect headers in a document data to tackle the problem of real. Such data that when used provides production quality models let ’ s COCO Challenge dataset, before training them our... To use deep learning when you complete the generation process once, it ’ talk... Point for making synthetic data, Uber sped up its neural architecture search ( ). Such data that significantly improves performance of computer vision Blender human labeling ( briefly ) an!

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