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| "Prototype-Grounded Concept Models (PGCMs) ground concept predictions in a fixed number of learned visual prototypes (m=30 for ColorMNIST+, m=120 for CelebA, m=100 for CLEVR-Hans) via a prototype selector, enabling inspection through concept alignment tables (Architecture & Components section).", | |
| "On ColorMNIST+, PGCM matches CBM performance with concept accuracy 99.2±0.0 vs 99.2±0.1 and task accuracy 99.7±0.0 vs 99.6±0.1, while on CelebA CBM's task accuracy (84.0±0.3) modestly exceeds PGCM's (83.0±0.0) (Table 4).", | |
| "PGCM training uses an ELBO-style objective (Equation 2) combining a KL regularization term over prototype selection, task loss, concept loss, and image reconstruction loss (Equation 2).", | |
| "On ColorMNIST+ with noisy labels, targeted prototype-level interventions improve accuracy from 92.9% to 96.9% by removing prototypes and from 92.8% to 97.8% by editing prototypes (Table 3).", | |
| "A prototype-swapping procedure applied halfway through training replaces learned prototype embeddings with their nearest training instances to improve interpretability (Interpretability Optimizations section).", | |
| "PGCM shows improved responsiveness to concept interventions compared to CBM, CRM, and CMR baselines, particularly when exploiting inter-concept dependencies in the PGCM* variant on ColorMNIST+ (Figure 4)." | |
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