Teacher-Anchored Selection of Post-Training Quantized Models under Domain Shift

arXiv:2609.31155 · cs.LG, cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Regret — 61× in this paper · explained in 3 episodes
• Accuracy — 54× in this paper · explained in 1 episode
• Alignment — 23× in this paper · explained in 1 episode
• Calibration — 21× in this paper · explained in 7 episodes
• Quantization — 19× in this paper · explained in 12 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Distillation — 8× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 4× in this paper
• Adam — 3× in this paper · explained in 2 episodes
• Domain Shift — 3× in this paper · explained in 4 episodes
• In International Conference on Learning Representations — 3× in this paper
• Transfer Learning — 3× in this paper · explained in 10 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• International Conference on Machine Learning — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Distilling the Knowledge in a Neural Network
• Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity

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