Framing Estimate Template
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AI Research News Update: Week 2 (Nov 22-30, 2021) Researchers At Trinity College Dublin and University Of Bath Introduce A Deep Neural Network-Based Model To Improve The Quality Of Animations Containing Quadruped Animals AI Research News Update: Week 2 (Nov 22-30, 2021) Researchers At Trinity College Dublin and University Of Bath Introduce A Deep Neural Network-Based Model To Improve The Quality Of Animations Containing Quadruped Animals It’s difficult to make realistic quadruped animations. Producing realistic animations with key-framing and other techniques takes a long time and demand a lot of artistic skill. Motion capture methods, on the other hand, have their own set of obstacles (bringing the animal into the studio, attaching motion capture markers, and getting the animal to perform the intended performance), and the final animation will almost certainly require cleanup. It would be beneficial if an animator could supply a rough animation and then be given a high-quality realistic one in exchange. It’s difficult to make realistic quadruped animations. Producing realistic animations with key-framing and other techniques takes a long time and demand a lot of artistic skill. Motion capture methods, on the other hand, have their own set of obstacles (bringing the animal into the studio, attaching motion capture markers, and getting the animal to perform the intended performance), and the final animation will almost certainly require cleanup. It would be beneficial if an animator could supply a rough animation and then be given a high-quality realistic one in exchange. Researchers at the University of Bath and Trinity College Dublin have developed a deep neural network-based technique that could help improve animations containing quadruped animals such as dogs. The team found that given an initial animation that may lack subtle details of true quadruped motion and/or contains small errors, a neural network can learn how to add these subtleties and correct errors to produce an enhanced animation while preserving the semantics and context of the original animation. Researchers at the University of Bath and Trinity College Dublin have developed a deep neural network-based technique that could help improve animations containing quadruped animals such as dogs. The team found that given an initial animation that may lack subtle details of true quadruped motion and/or contains small errors, a neural network can learn how to add these subtleties and correct errors to produce an enhanced animation while preserving the semantics and context of the original animation. Quick Read: https://www.marktechpost.com/2021/11/30/researchers-at-trinity-college-dublin-and-university-of-bath-introduce-a-deep-neural-network-based-model-to-improve-the-quality-of-animations-containing-quadruped-animals/ Microsoft Researchers Unlock New Avenues In Image-Generation Research With Manifold Matching Via Metric Learning Quick Read: https://www.marktechpost.com/2021/11/30/researchers-at-trinity-college-dublin-and-university-of-bath-introduce-a-deep-neural-network-based-model-to-improve-the-quality-of-animations-containing-quadruped-animals/ Microsoft Researchers Unlock New Avenues In Image-Generation Research With Manifold Matching Via Metric Learning By developing fresh images, generative image models provide a distinct value. These photos could be clear super-resolution copies of current images or even manufactured shots that look realistic. The framework of training two networks against each other has shown pioneering success with Generative Adversarial Networks (GANs) and their variants: a generator network learns to generate realistic fake data that can fool a discriminator network, and the discriminator network learns to correctly tell apart the generated counterfeit data from the actual data. By developing fresh images, generative image models provide a distinct value. These photos could be clear super-resolution copies of current images or even manufactured shots that look realistic. The framework of training two networks against each other has shown pioneering success with Generative Adversarial Networks (GANs) and their variants: a generator network learns to generate realistic fake data that can fool a discriminator network, and the discriminator network learns to correctly tell apart the generated counterfeit data from the actual data. The research community must address two issues in order to use the most recent advances in computer vision for GANs. First, rather than using geometric metrics, GANs model data distributions using statistical measures such as the mean and moments. Second, in classic GANs, the discriminator network loss is solely represented as a 1D scalar value corresponding to the Euclidean distance between the genuine and fake data distributions. The research community has been unable to utilize breakthrough metric learning methods directly or experiment with novel loss functions and training strategies to continue to develop generative models due to these two limitations. The research community must address two issues in order to use the most recent advances in computer vision for GANs. First, rather than using geometric metrics, GANs model data distributions using statistical measures such as the mean and moments. Second, in classic GANs, the discriminator network loss is solely represented as a 1D scalar value corresponding to the Euclidean distance between the genuine and fake data distributions. The research community has been unable to utilize breakthrough metric learning methods directly or experiment with novel loss functions and training strategies to continue to develop generative models due to these two limitations. Quick Read: https://www.marktechpost.com/2021/11/30/microsoft-researchers-unlock-new-avenues-in-image-generation-research-with-manifold-matching-via-metric-learning/ Google AI Improves The Performance Of Smart Text Selection Models By Using Federated Learning Quick Read: https://www.marktechpost.com/2021/11/30/microsoft-researchers-unlock-new-avenues-in-image-generation-research-with-manifold-matching-via-metric-learning/ Google AI Improves The Performance Of Smart Text Selection Models By Using Federated Learning Smart Text Selection is one of Android’s most popular features, assisting users in selecting, copying, and using text by anticipating the desired word or combination of words around a user’s tap and expanding the selection appropriately. Selections are automatically extended with this feature, and users are offered an app to open selections with defined classification categories, such as addresses and phone numbers, saving them even more time. Smart Text Selection is one of Android’s most popular features, assisting users in selecting, copying, and using text by anticipating the desired word or combination of words around a user’s tap and expanding the selection appropriately. Selections are automatically extended with this feature, and users are offered an app to open selections with defined classification categories, such as addresses and phone numbers, saving them even more time. The Google team made efforts to improve the performance of Smart Text Selection by utilizing federated learning to train a neural network model responsible for user interactions while maintaining personal privacy. The research team was able to enhance the model’s selection accuracy by up to 20% on some sorts of entities thanks to this effort, which is part of Android’s new Private Compute Core safe environment. The Google team made efforts to improve the performance of Smart Text Selection by utilizing federated learning to train a neural network model responsible for user interactions while maintaining personal privacy. The research team was able to enhance the model’s selection accuracy by up to 20% on some sorts of entities thanks to this effort, which is part of Android’s new Private Compute Core safe environment. Quick Read: https://www.marktechpost.com/2021/11/29/google-ai-improves-the-performance-of-smart-text-selection-models-by-using-federated-learning/ NVIDIA Open-Source ‘FLARE’ (Federated Learning Application Runtime Environment), Providing A Common Computing Foundation For Federated Learning Quick Read: https://www.marktechpost.com/2021/11/29/google-ai-improves-the-performance-of-smart-text-selection-models-by-using-federated-learning/ NVIDIA Open-Source ‘FLARE’ (Federated Learning Application Runtime Environment), Providing A Common Computing Foundation For Federated Learning
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