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Data Free Quantization

Data Free Quantization is a technique to achieve a highly accurate quantized model without accessing any training data.

Source: Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

Papers

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Showing 26–37 of 37 papers

TitleStatusHype
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization—0
Diversifying Sample Generation for Accurate Data-Free Quantization—0
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs—0
Enhancing Diversity for Data-free Quantization—0
Generative Zero-shot Network Quantization—0
MimiQ: Low-Bit Data-Free Quantization of Vision Transformers with Encouraging Inter-Head Attention Similarity—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare—0
QNNRepair: Quantized Neural Network Repair—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers—0
SPIQ: Data-Free Per-Channel Static Input Quantization—0
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