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A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
Google Test UI is a software tool for testing computer programs, and serves as a test runner. It employs a 'test binary', a compiled program responsible for executing tests and analyzing their results, to evaluate software functionality. It visually presents the testing progress through a progress bar and displays a list of identified issues or ...
Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment.
Tricentis Tosca is a software testing tool that is used to automate end-to-end testing for software applications.It is developed by Tricentis.. Tricentis Tosca combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective. [1]
General model-based testing setting Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment. The ...
This is a list of models and meshes commonly used in 3D computer graphics for testing and demonstrating rendering algorithms and visual effects. Their use is important for comparing results, similar to the way standard test images are used in image processing.
Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5] The U-Net architecture has also been employed in diffusion models for iterative image denoising. [6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.