Highlights: An image is just a matrix — but the Transformer eats sequences of vectors. The whole “vision” trick of ViT lives in how that matrix is turned into a sequence. We cut a 224×224 image into a fixed 14×14 grid of 16×16 patches, flatten each, and project it through ONE learned Linear layer. Patches are not sampled — every cell of the grid becomes a token, in fixed order. We prepend a learnable [CLS]…
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Highlights: Can three completely independent methods agree on which living room has the best colors? We rank 500 interior images using Cohen-Or harmonic templates, Hasler-Süsstrunk colorfulness, and CLIP IQA with 44 color-focused prompts — then measure whether they correlate at all. Method 1 — Cohen-Or: K-means palette → saturation-weighted hue histogram → sweep 7 harmonic templates × 36 rotations → H/T/S composite Method 2 — Hasler-Süsstrunk: opponent channels (rg, yb) → colorfulness + 4×4 spatial…
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Highlights: Every time you use Stable Diffusion, DALL-E, or Sora, the model never touches a single pixel during its main computation. It works entirely inside a compressed, structured space of floating-point numbers — a latent space learned by a VAE. In this post we build that space from scratch. We start from the simplest possible compression — an AutoEncoder on MNIST digits — understand why it fails at generation, fix it with the VAE’s probabilistic…
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