# Robust Quantization: One Model to Rule Them All Source: [Robust Quantization: One Model to Rule Them All]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:41:59.280Z Authors: Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex Bronstein, Uri Weiser Published: 2020-02-18T16:14:36.000Z ## Abstract Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise way quantization is performed. Robust quantization offers an alternative approach with improved tolerance to different classes of data-types and quantization policies. It opens up new exciting applications where the quantization process is not static and can vary to meet different circumstances and implementations. To address this issue, we propose a method that provides intrinsic robustness to the model against a broad range of quantization processes. Our method is motivated by theoretical arguments and enables us to store a single generic model capable of operating at various bit-widths and quantization policies. We validate our method's effectiveness on different ImageNet models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet-1K (val) (Top-1 Score)](): Image Classification; ImageNet-1k (val); Top-1 Acc: 72.5. ## Original sources and implementation [Original paper]() [PDF]()