Corruption Robustness in Post-Training Quantization of Vision Transformers: A Calibration Perspective

Post-training quantization (PTQ) enables efficient deployment of pretrained models using only a small calibration set, but its robustness under distribution shifts remains underexplored. We observe that quantized models retain substantially less of their full-precision performance on corrupted inputs than on clean data, particularly under low-bit quantization. We study this problem from the perspective of calibration data and show that different transformations of the same fixed-budget calibration set lead to markedly different corruption robustness. Our analysis reveals that effective transformations tend to reduce activation energy, exhibiting a characteristic energy shift similar to that observed under common corruptions. We associate this reduction with two changes in self-attention: lower attention concentration and weaker directional alignment among value vectors associated with different tokens. Motivated by these observations, we introduce *spatial entropy of difference energy*, a model-independent score that measures how broadly a transformation distributes its changes across an image. This score strongly correlates with both activation-energy reduction and post-quantization corruption robustness, enabling transformation selection without model inference. Based on this proxy, we propose **Entropy-$K$**, which selects multiple high-scoring transformations while preserving the original calibration budget. Entropy-$K$ requires neither corrupted target-domain samples nor modification of the underlying PTQ algorithm. Experiments across multiple Vision Transformer architectures and low-bit PTQ settings on ImageNet and ImageNet-C demonstrate consistent improvements in corruption robustness while maintaining competitive clean accuracy.