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: The paper provides a theoretical analysis of generalization errors and the impact of sample size on model performance.

: It focuses on making directional alignment (similar to cosine similarity) more robust in vision-language models.

: The method is designed to be "plug-and-play," meaning it doesn't require extra embeddings and works with various existing distillation frameworks. Core Methodology

: It reconfigures a shared space where both image and text features can be compared effectively.

The paper you are likely referring to, which features a diagram often displayed at

💡 : If you are looking for the implementation, the pseudocode is typically found in the Appendix of the full OpenReview document. AME: ALIGNED MANIFOLD ENTROPY FOR ROBUST - OpenReview