Jihae Lee

When and How to Rotate: Loss Design and Activation Granularity for Rotation-Based Vision Model Quantization

Orthogonal-transformation-based post-training quantization (PTQ) mitigates activation outliers in large language models (LLMs), but its use for vision models remains unclear: when learned rotations are necessary, which training objectives are stable, and how activation quantization granularity affects performance. We e...