Search Results for "deep" - Page 32
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Deep Exemplar-based Video Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization". End-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. ... -
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DeepScaleTool
Estimation of deep-submicron technology scaling
...To cite the paper - https://ieeexplore.ieee.org/abstract/document/9401196 S. Sarangi and B. Baas, "DeepScaleTool: A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era," 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, pp. 1-5, doi: 10.1109/ISCAS51556.2021.9401196. -
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VRWorkout
High-intensity virtual reality workout game
The game tracks your running speed and awards more points the faster you go. Arm movements to the beat of the music and fast ducking to avoid bad targets. Shallow or deep squats depending on your comfort level. Engange your core muscles by reaching for higher targets. Pushups followed by jumps get your heart pumping. Classic pushup, one-armed planks and side planks. Fast and small jumps similar to skip rope jumping. A “workout first” VR music game with hand tracking based full-body exercises in boot camp style workouts, your fitness wins regardless of your score. ... -
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PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target detection, image segmentation, text recognition, speech synthesis, etc. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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...The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. ...
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Notepad++ UDL2 Color syntax for SAS
Notepad++ UDL UDL2 User Defined Language color syntax for SAS language
... - Keyword style by group and type, easy customisation. - Very good support for all SAS format. For all dark background color syntax, set Notepad++ theme to "Deep Black", "Global Styles", "Global Override". Include an Excel document with all SAS Keywords and Operators by category and Syntax coloring group number. Multi-lines comment, as long as the last character of each line is not a whitespace character Notepad++ will correctly handle syntax colouring. NEW: -Workaround for regular expression containing not matched quote. ... -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
For quite some time now, we know about the benefits of transfer learning in Computer Vision (CV) applications. Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. ... -
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Pop Shell
Keyboard-driven layer for GNOME Shell
Pop Shell is a core component of the Pop!_OS desktop experience that extends and customizes the GNOME Shell environment to provide enhanced usability, productivity, and workflow management. It introduces features such as automatic tiling window management, workspace organization, and keyboard-driven navigation, transforming the traditional GNOME interface into a more efficient and developer-friendly environment. The project acts as a bridge between the base GNOME system and the customized... -
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Surface Defect Detection Dataset Papers
Constantly summarizing open source dataset and critical papers
...A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. -
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/tg/station
The /tg/station branch of SS13
...Space Station 13 is a paranoia-laden round-based roleplaying game set against the backdrop of a nonsensical, metal death trap masquerading as a space station, with charming spritework designed to represent the sci-fi setting and its dangerous undertones. Have fun, and survive. This is the codebase for the /tg/station flavoured fork of SpaceStation 13. Deep simulation systems including chemistry, atmosphere, power, and biology. -
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ALAE
Adversarial Latent Autoencoders
ALAE (Adversarial Latent Autoencoders) is a deep learning research implementation that combines autoencoders with generative adversarial networks to produce high-quality image synthesis models. The project implements the architecture introduced in the CVPR research paper on Adversarial Latent Autoencoders, which focuses on improving generative modeling by learning latent representations aligned with adversarial training objectives. -
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BioNetExplorer
Framework for Exploration of Bio-signal Processing DNNs in Wearables
...In case of usage, please refer to: B. S. Prabakaran, A. Akhtar, S. Rehman, O. Hasan, M. Shafique, “BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables,” IEEE Internet of Things Journal, 2021. -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. ... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. -
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Robust Tube MPC
Example implementation for robust model predictive control using tube
robust-tube-mpc is a MATLAB implementation of robust tube-based Model Predictive Control (MPC). The framework provides tools to design and simulate controllers that maintain stability and constraint satisfaction in the presence of bounded disturbances. Tube-based MPC achieves robustness by combining a nominal trajectory planner with an error feedback controller that keeps the actual system state within a "tube" around the nominal trajectory. This repository includes example scripts and... -
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OpenAI Glow
Copy code in "Glow: Generative Flow with Invertible 1x1 Convolutions"
...The repository provides training code, pretrained models, and scripts for generating samples or reproducing key results from the original research. Glow is primarily intended for researchers and practitioners exploring generative modeling, likelihood-based training, and interpretable deep learning systems. -
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3 Rs of Software Architecture
A guide on how to write readable, reusable, and refactorable software
...The project uses a simple shopping-cart application written in JavaScript and React/Redux to illustrate how code evolves from “bad” via “better” to “good” across those three dimensions. By examining common smells—poor naming, deep nesting, long functions, tight coupling—readers learn how to restructure code to improve maintainability. It is targeted at developers of any experience level, though beginners will find its concrete examples especially useful. The tutorial-style repository includes code with comments and comparisons between less readable and more readable versions, promoting intentional design. -
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Geroes is a deep learning-based system dedicated to evaluating gene contribution in defining tissue specificity.
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DeepMind Lab
A customizable 3D platform for agent-based AI research
...DeepMind Lab provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning. If you use DeepMind Lab in your research and would like to cite the DeepMind Lab environment, we suggest you cite the DeepMind Lab paper. To enable compiler optimizations, pass the flag --compilation_mode=opt, or -c opt for short, to each bazel build, bazel test and bazel run command. The flag is omitted from the examples here for brevity, but it should be used for real training and evaluation where performance matters. ... -
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Effective TensorFlow 2
TensorFlow tutorials and best practices
...The tutorials emphasize both conceptual understanding and implementation details so that users can build more robust deep learning systems. -
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GIMP ML
AI for GNU Image Manipulation Program
...In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
...If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. ... -
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JadeOak-OS
An operating system for those who value security and privacy.
Welcome to JadeOak OS, an operating system for those that value simplicity and privacy. It has the deep web browser, also known as the Tor browser, added to it as a default internet browser. It is linux-based and will work on a virtual machine, such as VirtualBox. For Installation Instructions or any issues with the OS, please go to the wiki. There is more detailed information about the OS on the wiki also. -
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DCGAN in Tensorflow
Deep Convolutional Generative Adversarial Networks
DCGAN-tensorflow is a classic TensorFlow implementation of Deep Convolutional Generative Adversarial Networks, intended to demonstrate and reproduce the stabilized GAN architecture described in the original research. The repository provides complete training scripts, model definitions, and utilities for generating synthetic images from datasets such as MNIST and CelebA. It serves both as an educational reference and as a practical starting point for developers experimenting with generative models. ... -
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker. For inference, see SageMaker MXNet Inference Toolkit. For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. ...
