[6] DLAA collects game rendering data including raw low-resolution input, motion vectors, depth buffers, and exposure information.
The training dataset includes diverse scenarios focusing on challenging cases like sub-pixel details, high-contrast edges, and transparent surfaces.
[3] Unlike traditional anti-aliasing solutions that rely on manually written heuristics, such as TAA, DLAA uses its neural network to preserve fine details while eliminating unwanted visual artifacts.
This helps to identify and fix many temporal artifacts, but deliberately removing fine details in this way is analogous to applying a blur filter, and thus the final image can appear blurry when using this method.
[8] DLAA uses an auto-encoder convolutional neural network[9] trained to identify and fix temporal artifacts, instead of manually programmed heuristics as mentioned above.