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Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. [1] DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. [1]DLAA is similar to Deep Learning Super Sampling (DLSS) in its anti-aliasing method, [2] with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, [3] whereas the ...
The data DLSS 2.0 collects includes: the raw low-resolution input, motion vectors, depth buffers, and exposure / brightness information. [13] It can also be used as a simpler TAA implementation where the image is rendered at 100% resolution, rather than being upsampled by DLSS, Nvidia brands this as DLAA (Deep Learning Anti-Aliasing). [26]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
[42] [43] [44] The PC version of the remaster boasts numerous specific improvements, such as NVIDIA DLSS and DLAA support, compatibility with ultrawide and panoramic display monitors, an additional presentation preset known as "Ultimate RT" for capable NVIDIA and AMD graphics cards, native support for various controllers and mouse & keyboard ...
Evaluators often tailor their evaluations to produce results that can have a direct influence in the improvement of the structure, or on the process, of a program. For example, the evaluation of a novel educational intervention may produce results that indicate no improvement in students' marks.
The Test and Evaluation Master Plan documents the overall structure and objectives of the Test & Evaluation for a program. [3] It covers activities over a program’s life-cycle and identifies evaluation criteria for the testers. [4] The test and evaluation master plan consists of individual tests. Each test contains the following. Test Scenario
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Summative evaluation judges the worth or value of an educational unit of study at its conclusion. Summative assessments also serve the purpose of evaluating student learning. In schools, these assessments varies: traditional written tests, essays, presentations, discussions, or reports using other formats. [3]