AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A cross-modal distillation framework that enables effective knowledge transfer across structurally heterogeneous feature spaces via a vector-quantized codebook, and the selected codes serve as concept-level anchors for student learning is proposed.
Concept-Guided Prediction Refinement (CGPR), a novel training-free framework for zero-shot painting style classification, is proposed, which consistently improves both Top-1 accuracy and balanced accuracy over vanilla zero-shot inference without requiring additional labeled data or model fine-tuning.
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