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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 1–25 of 37 papers

TitleStatusHype
LLM-QAT: Data-Free Quantization Aware Training for Large Language ModelsCode3
Patch Similarity Aware Data-Free Quantization for Vision TransformersCode1
Adaptive Data-Free QuantizationCode1
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision TransformersCode1
Qimera: Data-free Quantization with Synthetic Boundary Supporting SamplesCode1
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
Generative Low-bitwidth Data Free QuantizationCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
DVD-Quant: Data-free Video Diffusion Transformers QuantizationCode1
Towards Accurate Post-training Quantization for Diffusion ModelsCode1
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the TeacherCode1
ZeroQ: A Novel Zero Shot Quantization FrameworkCode1
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian ApproximationCode1
Genie: Show Me the Data for QuantizationCode0
Rethinking Data-Free Quantization as a Zero-Sum GameCode0
Causal-DFQ: Causality Guided Data-free Network QuantizationCode0
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer—0
Zero-shot Adversarial Quantization—0
ACQ: Improving Generative Data-free Quantization Via Attention Correction—0
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization—0
A method of using RSVD in residual calculation of LowBit GEMM—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Data-Free Quantization via Pseudo-label Filtering—0
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