Sampling latents from the output GMM to synthesize random inputs
caglar_ee · x · 2026-11-11
Extending his thread: since the K-means classifier is a GMM special case, random inputs can be generated by sampling latent features yₙ from the output GMM, then sampling xₙ from the feature extractor's p(xₙ|yₙ). This closes the loop on data generation within the alternating-minimization framework, unifying classification, OOD detection, and sample synthesis under one generative view.
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