All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models
<|reference_start|>All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models: Do different generative image models secretly learn similar underlying representations? We investigate this by measuring the latent space similarity of four different models: VAEs, GANs, Normalizing Flows (NFs), and Diffusion Models (DMs). Our methodology involves training linear maps between frozen latent spaces to "stitch" arbitrary pairs of encoders and decoders and measuring output-based and probe-based metrics on the resulting "stitched'' models. Our main findings are that linear maps between latent spaces of performant models preserve most visual information even when latent sizes differ; for CelebA models, gender is the most similarly represented probe-able attribute. Finally we show on an NF that latent space representations converge early in training.<|reference_end|>
@article{badrinath2024all,
    title={All Roads Lead to Rome? Exploring Representational Similarities Between
  Latent Spaces of Generative Image Models},
    author={Charumathi Badrinath, Usha Bhalla, Alex Oesterling, Suraj Srinivas,
  Himabindu Lakkaraju},
    journal={arXiv preprint arXiv:2407.13449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13449},
    primaryClass={cs.LG}
}