GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

arXiv:2506.11784 · cs.CV · Submitted 2025-06-13 · Read on arXiv

cs.CV

Submitted: 2025-06-13

Updated: 2026-09-21

Terminology

• Quantization — 96× in this paper · explained in 12 episodes
• Accuracy — 48× in this paper · explained in 1 episode
• Generalization — 41× in this paper · explained in 13 episodes
• Transformer — 24× in this paper · explained in 2 episodes
• Downstream Tasks — 13× in this paper · explained in 2 episodes
• Distillation — 9× in this paper · explained in 2 episodes
• Post-Training Quantization — 8× in this paper
• Quantization-Aware Training — 6× in this paper · explained in 3 episodes
• Core — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• Knowledge Distillation — 4× in this paper · explained in 11 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Instance Segmentation — 2× in this paper · explained in 3 episodes
• Linear Probing — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Straight-Through Estimator — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Principal Component Analysis — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Qwen Technical Report
• EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Learned Step Size Quantization
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• Quantization without Tears
• Distilling the Knowledge in a Neural Network
• Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision
• AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers
• Decoupled Weight Decay Regularization
• Fine-Grained Visual Classification of Aircraft
• LLaMA: Open and Efficient Foundation Language Models

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