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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]
Nvidia's DLSS operates on similar principles to TAA. Like TAA, it uses information from past frames to produce the current frame. Unlike TAA, DLSS does not sample every pixel in every frame. Instead, it samples different pixels in different frames and uses pixels sampled in past frames to fill in the unsampled pixels in the current frame.
The input data is the rendered image and optionally the luminance data. [3]Acquire the luminance data. [3] This data could be passed into the FXAA algorithm from the rendering step as an alpha channel embedded into the image to be antialiased, calculated from the rendered image, or approximated by using the green channel as the luminance data.
Morphological antialiasing (MLAA) is a technique for minimizing the distortion artifacts known as aliasing when representing a high-resolution image at a lower resolution. ...
To assess performance, it is necessary to select, before the implementation of the project, indicators which will permit to rate the targeted outputs and outcomes. According to the United Nations Development Programme (UNDP), an outcome indicator has two components: the baseline which is the situation before the programme or project begins, and ...
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]
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