Data to 14 Oct 2026

Robust Quantization: One Model to Rule Them All

arXiv 2002.07686 ↗First posted 18 Feb 2020cs.LG cs.CV stat.MLcited by 63

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Abstract (arXiv)

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.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 9; 2023: 7; 2024: 14; 2025: 13; 2026: 9.

63 papers.

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2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
2022Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Modelscited by 181
2023Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothingcited by 151
2024FlatQuant: Flatness Matters for LLM Quantizationcited by 131
2024ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Cachingcited by 48
2024Outlier-Efficient Hopfield Layers for Large Transformer-Based Modelscited by 38
2022NIPQ: Noise proxy-based Integrated Pseudo-Quantizationcited by 35
2022SDQ: Stochastic Differentiable Quantization with Mixed Precisioncited by 28
2021Pareto-Optimal Quantized ResNet Is Mostly 4-bitcited by 26
2021Anchor-based Plain Net for Mobile Image Super-Resolutioncited by 24
2024Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularizationcited by 23
2024Towards Accurate Post-Training Quantization of Vision Transformers via Error Reductioncited by 23

38 more

2021BatchQuant: Quantized-for-all Architecture Search with Robust Quantizercited by 20
2021Post-Training Sparsity-Aware Quantizationcited by 17
2023Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precisioncited by 17
2021Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamicscited by 12
2022Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devicescited by 12
2025KurTail : Kurtosis-based LLM Quantizationcited by 12
2025Low-bit Model Quantization for Deep Neural Networks: A Surveycited by 11
2023EQ-Net: Elastic Quantization Neural Networkscited by 10
2025Fast and Low-Cost Genomic Foundation Models via Outlier Removalcited by 8
2025Value-State Gated Attention for Mitigating Extreme-Token Phenomena in Transformerscited by 8
2024QGen: On the Ability to Generalize in Quantization Aware Trainingcited by 7
2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applicationscited by 7
2023MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantizationcited by 6
2023Post-Training Quantization for Re-parameterization via Coarse & Fine Weight Splittingcited by 6
2026LoopQ: Quantization for Looped Language Modelscited by 6
20212-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiencycited by 5
2024FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Modelscited by 5
2022Bimodal Distributed Binarized Neural Networkscited by 4
2024MCNC: Manifold-Constrained Reparameterization for Neural Compressioncited by 4
2021One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustmentcited by 3
2022What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Studycited by 3
2022Symmetry Regularization and Saturating Nonlinearity for Robust Quantizationcited by 3
2022BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptronscited by 3
2023R2 Loss: Range Restriction Loss for Model Compression and Quantizationcited by 3
2025Analog Foundation Modelscited by 3
2025TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precisioncited by 3
2025Moment- and Power-Spectrum-Based Gaussianity Regularization for Text-to-Image Modelscited by 3
2025Efficient Multi-bit Quantization Network Training via Weight Bias Correction and Bit-wise Coreset Samplingcited by 3
2025Bandwidth-Efficient Adaptive Mixture-of-Experts via Low-Rank Compensationcited by 3
2024The Impact of Quantization on the Robustness of Transformer-based Text Classifierscited by 2
2024QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuningcited by 2
2024Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Informationcited by 2
2024Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Searchcited by 2
2026Gradient Preconditioning for Efficient and Reliable Reward-Guided Generationcited by 2
2026Attention Sinks and Outliers in Attention Residualscited by 2
2026MixQuant: Adaptive Mixed-Precision Quantization for Large Language Modelscited by 2
2021RCT: Resource Constrained Training for Edge AIcited by 1
2022Weight Fixing Networkscited by 1

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