# NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation

arXiv: https://arxiv.org/abs/2310.00258 · HTML: https://mlsanity.com/paper/2310.00258/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [ZeroQ: A Novel Zero Shot Quantization Framework](https://mlsanity.com/paper/2001.00281/) (2020)
- [Focal and Global Knowledge Distillation for Detectors](https://mlsanity.com/paper/2111.11837/) (2021)
- [Zero-shot Knowledge Transfer via Adversarial Belief Matching](https://mlsanity.com/paper/1905.09768/) (2019)
- [Zero-Shot Knowledge Distillation in Deep Networks](https://mlsanity.com/paper/1905.08114/) (2019)
- [Data-Free Network Quantization With Adversarial Knowledge Distillation](https://mlsanity.com/paper/2005.04136/) (2020)
- [Contrastive Model Inversion for Data-Free Knowledge Distillation](https://mlsanity.com/paper/2105.08584/) (2021)
- [Zero-shot Adversarial Quantization](https://mlsanity.com/paper/2103.15263/) (2021)
- [Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation](https://mlsanity.com/paper/2206.06067/) (2022)
- [Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay](https://mlsanity.com/paper/2201.03019/) (2022)
- [Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data](https://mlsanity.com/paper/2108.05698/) (2021)
- [Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation](https://mlsanity.com/paper/2209.10359/) (2022)
- [Large-Scale Generative Data-Free Distillation](https://mlsanity.com/paper/2012.05578/) (2020)
- [Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation](https://mlsanity.com/paper/2302.14290/) (2023)

## Built on by (most cited first, 22 of 22)

- [Text-Enhanced Data-free Approach for Federated Class-Incremental Learning](https://mlsanity.com/paper/2403.14101/) (2024)
- [Data-free Knowledge Distillation with Diffusion Models](https://mlsanity.com/paper/2504.00870/) (2025)
- [ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://mlsanity.com/paper/2405.14608/) (2024)
- [What Makes a Good Dataset for Knowledge Distillation?](https://mlsanity.com/paper/2411.12817/) (2024)
- [Enhancing Dataset Distillation via Non-Critical Region Refinement](https://mlsanity.com/paper/2503.18267/) (2025)
- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)
- [When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need](https://mlsanity.com/paper/2507.04119/) (2025)
- [Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation](https://mlsanity.com/paper/2411.17046/) (2024)
- [Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning](https://mlsanity.com/paper/2510.26311/) (2025)
- [CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation](https://mlsanity.com/paper/2504.21478/) (2025)
- [Image Recognition with Online Lightweight Vision Transformer: A Survey](https://mlsanity.com/paper/2505.03113/) (2025)
- [Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments](https://mlsanity.com/paper/2505.19699/) (2025)
- [Holmes: Towards Effective and Harmless Model Ownership Verification to Personalized Large Vision Models via Decoupling Common Features](https://mlsanity.com/paper/2507.00724/) (2025)
- [PRISM: Precision-Recall Informed Data-Free Knowledge Distillation via Generative Diffusion](https://mlsanity.com/paper/2509.16897/) (2025)
- [Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks](https://mlsanity.com/paper/2504.00540/) (2025)
- [When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class](https://mlsanity.com/paper/2506.15381/) (2025)
- [Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling](https://mlsanity.com/paper/2603.27665/) (2026)
- [Diverse Image Priors for Black-box Data-free Knowledge Distillation](https://mlsanity.com/paper/2604.25794/) (2026)
- [STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation](https://mlsanity.com/paper/2605.27409/) (2026)
- [Gradient Transformer: Learning to Generate Updates for LLMs](https://mlsanity.com/paper/2605.27591/) (2026)
- [UniDFKD: A Unified Semantic Prior Framework for Architecture-Agnostic Data-Free Knowledge Distillation](https://mlsanity.com/paper/2608.09287/) (2026)
- [Close Shortcut Wins Long: Seeking Diverse and Stable Generators for Data-Free Knowledge Distillation](https://mlsanity.com/paper/2608.22003/) (2026)

## Abstract (arXiv)

Data-Free Knowledge Distillation (DFKD) has made significant recent strides by transferring knowledge from a teacher neural network to a student neural network without accessing the original data. Nonetheless, existing approaches encounter a significant challenge when attempting to generate samples from random noise inputs, which inherently lack meaningful information. Consequently, these models struggle to effectively map this noise to the ground-truth sample distribution, resulting in prolonging training times and low-quality outputs. In this paper, we propose a novel Noisy Layer Generation method (NAYER) which relocates the random source from the input to a noisy layer and utilizes the meaningful constant label-text embedding (LTE) as the input. LTE is generated by using the language model once, and then it is stored in memory for all subsequent training processes. The significance of LTE lies in its ability to contain substantial meaningful inter-class information, enabling the generation of high-quality samples with only a few training steps. Simultaneously, the noisy layer plays a key role in addressing the issue of diversity in sample generation by preventing the model from overemphasizing the constrained label information. By reinitializing the noisy layer in each iteration, we aim to facilitate the generation of diverse samples while still retaining the method's efficiency, thanks to the ease of learning provided by LTE. Experiments carried out on multiple datasets demonstrate that our NAYER not only outperforms the state-of-the-art methods but also achieves speeds 5 to 15 times faster than previous approaches. The code is available at https://github.com/tmtuan1307/nayer.