Investigating Permutation-Invariant Discrete Representation Learning for Spatially Aligned Images
Vector quantization approaches (VQ-VAE, VQ-GAN) learn discrete neural representations of images, but these representations are inherently position-dependent: codes are spatially arranged and contextually entangled, requiring autoregressive or diffusion-based priors to model their dependencies at sample time. In this work, we ask whether positional information is necessary for discrete representations of spatially aligned data. We propose the permutation-invariant vector-quantized autoencoder (PI-VQ), in which latent codes are constrained to carry no positional information. We find that this constraint encourages codes to capture global, semantic features, and enables direct interpolation ...
| Authors | Jamie S. J. Stirling, Noura Al-Moubayed, Hubert P. H. Shum |
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| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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