Data Free Quantization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (37 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Data-Free Quantization Through Weight Equalization and Bias Correction
  11 Jun 2019 5 repositories listed
  This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call.
• LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
  29 May 2023 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits.
• Generative Low-bitwidth Data Free Quantization
  7 Mar 2020 3 repositories listed
  More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method.
• ZeroQ: A Novel Zero Shot Quantization Framework
  1 Jan 2020 3 repositories listed Syntology ran 4 of 19 samples · 15 unverified
  Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0.
• Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
  4 Nov 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries.
• DVD-Quant: Data-free Video Diffusion Transformers Quantization
  24 May 2025 1 repository listed
  Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment.
• Relation-Guided Adversarial Learning for Data-free Knowledge Transfer
  16 Dec 2024 1 repository listed
  Then, in the student training phase, we perform an opposite optimization, which adversarially attempts to reduce the distance of samples of the same classes and enlarge the distance of samples of different classes.
• Causal-DFQ: Causality Guided Data-free Network Quantization
  24 Sep 2023 1 repository listed
  Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened…
• Towards Accurate Post-training Quantization for Diffusion Models
  30 May 2023 1 repository listed Syntology ran 5 of 5 samples · 0 unverified · 2 pointer-only (licence)
  On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized…
• Adaptive Data-Free Quantization
  13 Mar 2023 1 repository listed
  Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is…
• Rethinking Data-Free Quantization as a Zero-Sum Game
  19 Feb 2023 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  how to generate the samples with desirable adaptability to benefit the quantized network?
• PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers
  13 Sep 2022 1 repository listed Syntology ran 5 of 8 samples · 3 unverified · 1 pointer-only (licence)
  In this paper, we propose PSAQ-ViT V2, a more accurate and general data-free quantization framework for ViTs, built on top of PSAQ-ViT.
• It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
  31 Mar 2022 1 repository listed
  To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks.
• Patch Similarity Aware Data-Free Quantization for Vision Transformers
  4 Mar 2022 1 repository listed
  The above insights guide us to design a relative value metric to optimize the Gaussian noise to approximate the real images, which are then utilized to calibrate the quantization parameters.
• SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
  14 Feb 2022 1 repository listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements.
• Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization
  1 Sep 2021 1 repository listed
  We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics…

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.