The ByteFormer model you're discussing sounds pretty groundbreaking! It's fascinating how it can classify images directly from TIFF file bytes with such high accuracy (77.33% on ImageNet), beating traditional methods that work on RGB images. Even more impressive is its ability to handle WAV files from the Speech Commands v2 dataset with minimal effort, scoring a 95.42% classification accuracy.
The part about privacy-preserving inference is super intriguing. Operating on obfuscated inputs without losing accuracy could be a game-changer for data privacy. The idea of a privacy-preserving camera that ByteFormer can work with, even when 90% of pixel channels are masked, yet still pulling off a 71.35% accuracy, is pretty cool.
The ByteFormer model you're discussing sounds pretty groundbreaking! It's fascinating how it can classify images directly from TIFF file bytes with such high accuracy (77.33% on ImageNet), beating traditional methods that work on RGB images. Even more impressive is its ability to handle WAV files from the Speech Commands v2 dataset with minimal effort, scoring a 95.42% classification accuracy.
The part about privacy-preserving inference is super intriguing. Operating on obfuscated inputs without losing accuracy could be a game-changer for data privacy. The idea of a privacy-preserving camera that ByteFormer can work with, even when 90% of pixel channels are masked, yet still pulling off a 71.35% accuracy, is pretty cool.
Regarding the PNG to... (read more)