  Do different neural networks, trained for various vision tasks, share some
common representations? In this paper, we demonstrate the existence of common
features we call "Rosetta Neurons" across a range of models with different
architectures, different tasks (generative and discriminative), and different
types of supervision (class-supervised, text-supervised, self-supervised). We
present an algorithm for mining a dictionary of Rosetta Neurons across several
popular vision models: Class Supervised-ResNet50, DINO-ResNet50, DINO-ViT, MAE,
CLIP-ResNet50, BigGAN, StyleGAN-2, StyleGAN-XL. Our findings suggest that
certain visual concepts and structures are inherently embedded in the natural
world and can be learned by different models regardless of the specific task or
architecture, and without the use of semantic labels. We can visualize shared
concepts directly due to generative models included in our analysis. The
Rosetta Neurons facilitate model-to-model translation enabling various
inversion-based manipulations, including cross-class alignments, shifting,
zooming, and more, without the need for specialized training.
