Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization

ArXi:2605.16423v1 Announce Type: new Network quantization has emerged as one of the most practical model compression techniques, which significantly reduces a model's memory and compute consumption by mapping floating-point numbers to low-bit representations. However, existing quantization methods typically suffer from the speed-accuracy tradeoff and limited generalization. To address these issues, recent compensation-based methods offer an efficient yet general solution by