# Masked Autoencoders Enable Efficient Knowledge Distillers

arXiv: https://arxiv.org/abs/2208.12256 · HTML: https://mlsanity.com/paper/2208.12256/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 33 of its 38 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [Masked Feature Prediction for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2112.09133/) (2021)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [Born Again Neural Networks](https://mlsanity.com/paper/1805.04770/) (2018)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)
- [A Comprehensive Overhaul of Feature Distillation](https://mlsanity.com/paper/1904.01866/) (2019)
- [Paraphrasing Complex Network: Network Compression via Factor Transfer](https://mlsanity.com/paper/1802.04977/) (2018)
- [Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons](https://mlsanity.com/paper/1811.03233/) (2018)
- [DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers](https://mlsanity.com/paper/2204.12997/) (2022)
- [Glance-and-Gaze Vision Transformer](https://mlsanity.com/paper/2106.02277/) (2021)

## Built on by (most cited first, 30 of 36)

- [EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://mlsanity.com/paper/2312.00863/) (2023)
- [Stare at What You See: Masked Image Modeling without Reconstruction](https://mlsanity.com/paper/2211.08887/) (2022)
- [Masked Image Modeling: A Survey](https://mlsanity.com/paper/2408.06687/) (2024)
- [FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning](https://mlsanity.com/paper/2412.12032/) (2024)
- [Generic-to-Specific Distillation of Masked Autoencoders](https://mlsanity.com/paper/2302.14771/) (2023)
- [Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts](https://mlsanity.com/paper/2402.05382/) (2024)
- [Turbo-VAED: Fast and Stable Transfer of Video-VAEs to Mobile Devices](https://mlsanity.com/paper/2508.09136/) (2025)
- [CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation](https://mlsanity.com/paper/2305.11481/) (2023)
- [Masked Modeling for Self-supervised Representation Learning on Vision and Beyond](https://mlsanity.com/paper/2401.00897/) (2023)
- [Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision](https://mlsanity.com/paper/2404.15672/) (2024)
- [Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking](https://mlsanity.com/paper/2303.05475/) (2023)
- [It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training](https://mlsanity.com/paper/2210.05234/) (2022)
- [SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining](https://mlsanity.com/paper/2404.01156/) (2024)
- [Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression](https://mlsanity.com/paper/2403.15835/) (2024)
- [Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation](https://mlsanity.com/paper/2602.19161/) (2026)
- [Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers](https://mlsanity.com/paper/2404.09326/) (2024)
- [EmbodiedMAE: A Unified 3D Multi-Modal Representation for Robot Manipulation](https://mlsanity.com/paper/2505.10105/) (2025)
- [Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation](https://mlsanity.com/paper/2505.15581/) (2025)
- [MOMA:Distill from Self-Supervised Teachers](https://mlsanity.com/paper/2302.02089/) (2023)
- [Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution](https://mlsanity.com/paper/2404.02573/) (2024)
- [Aerial Image Object Detection With Vision Transformer Detector (ViTDet)](https://mlsanity.com/paper/2301.12058/) (2023)
- [Why does Knowledge Distillation Work? Rethink its Attention and Fidelity Mechanism](https://mlsanity.com/paper/2405.00739/) (2024)
- [Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets](https://mlsanity.com/paper/2410.22184/) (2024)
- [UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation](https://mlsanity.com/paper/2502.02257/) (2025)
- [Remote Sensing Scene Classification with Masked Image Modeling (MIM)](https://mlsanity.com/paper/2302.14256/) (2023)
- [OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System](https://mlsanity.com/paper/2303.00501/) (2023)
- [Asymmetric Masked Distillation for Pre-Training Small Foundation Models](https://mlsanity.com/paper/2311.03149/) (2023)
- [SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival](https://mlsanity.com/paper/2403.09290/) (2024)
- [MRC-DETR: An Adaptive Multi-Residual Coupled Transformer for Bare Board PCB Defect Detection](https://mlsanity.com/paper/2507.03386/) (2025)
- [From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations](https://mlsanity.com/paper/2508.15404/) (2025)

## Abstract (arXiv)

This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel reconstruction loss on masked inputs, we minimize the distance between the intermediate feature map of the teacher model and that of the student model. This design leads to a computationally efficient knowledge distillation framework, given 1) only a small visible subset of patches is used, and 2) the (cumbersome) teacher model only needs to be partially executed, ie, forward propagate inputs through the first few layers, for obtaining intermediate feature maps. Compared to directly distilling fine-tuned models, distilling pre-trained models substantially improves downstream performance. For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84.0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1.2%. More intriguingly, our method can robustly distill knowledge from teacher models even with extremely high masking ratios: e.g., with 95% masking ratio where merely TEN patches are visible during distillation, our ViT-B competitively attains a top-1 ImageNet accuracy of 83.6%; surprisingly, it can still secure 82.4% top-1 ImageNet accuracy by aggressively training with just FOUR visible patches (98% masking ratio). The code and models are publicly available at https://github.com/UCSC-VLAA/DMAE.