AMD’s FSR 2.0 is John Henry versus Nvidia DLSS’s machine learning
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The image-quality battle brewing between AMD and Nvidia is heating up. And this week, the back-and-forth regarding Nvidia’s deep-learning super sampling (DLSS) and AMD’s FidelityFX Super Resolution (FSR) is taking on a human-versus-machine angle. To be more specific, AMD believes the humans responsible for FSR are sometimes more effective than the machine learning that Nvidia uses for DLSS.
AMD introduced FSR 2.0 last week, and it emphasized that it isn’t using any machine learning to accomplish its impressive upscaling results in games like Deathloop. Now, AMD is explaining a bit more about why it is using hand-coded algorithms as opposed to DLSS, which uses a method where a computer trains itself to recognize what a perfect frame should look like at the highest possible resolution.
“In any science, which includes software engineering, discoveries are made by analyzing data from experimentation, resulting in mathematical models that can explain the results,” reads an AMD blog post. “Broadly speaking, machine learning (ML) is an incredibly useful set of tools and techniques that can aid and accelerate this process. However, the results that ML achieves can sometimes not be the most optimal, lacking the spark of human imagination that can often lead to breakthroughs for complex problems.”
Basically, AMD thinks humans are already good at solving the problems that upscaling introduces. And it claims that it has more flexibility with prescribing solutions to certain scenarios than it would if it adopted the DLSS model.
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