Masked Autoencoders Are Scalable Vision Learners

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pre-training and shows promising scaling behavior.

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Semantic Segmentation ADE20K MAE (ViT-B, UperNet) Validation mIoU 48.1 # 93
Semantic Segmentation ADE20K MAE (ViT-L, UperNet) Validation mIoU 53.6 # 45
Object Detection COCO minival MAE (ViT-L, Mask R-CNN) box AP 53.3 # 39
Object Detection COCO minival MAE (ViT-B, Mask R-CNN) box AP 50.3 # 52
Image Classification ImageNet MAE (ViT-L) Top 1 Accuracy 83.6% # 268
Top 1 Accuracy 85.9% # 131
Self-Supervised Image Classification ImageNet MAE (ViT-B) Top 1 Accuracy 68.0% # 76
Self-Supervised Image Classification ImageNet MAE (ViT-L) Top 1 Accuracy 75.8% # 41
Self-Supervised Image Classification ImageNet MAE (ViT-H) Top 1 Accuracy 76.6% # 34
Image Classification ImageNet MAE (ViT-H, 448) Top 1 Accuracy 87.8% # 52
Number of params 656M # 766
Image Classification ImageNet MAE (ViT-H) Top 1 Accuracy 86.9% # 82
Domain Generalization ImageNet-A MAE (ViT-H, 448) Top-1 accuracy % 76.7 # 5
Domain Generalization ImageNet-C MAE (ViT-H) mean Corruption Error (mCE) 33.8 # 2
Number of params 632M # 32
Self-Supervised Image Classification ImageNet (finetuned) MAE (ViT-H/14, 448) Number of Params 632M # 5
Top 1 Accuracy 87.8% # 6
Self-Supervised Image Classification ImageNet (finetuned) MAE (ViT-H/14) Top 1 Accuracy 86.9% # 9
Domain Generalization ImageNet-R MAE (ViT-H, 448) Top-1 Error Rate 33.5 # 6
Domain Generalization ImageNet-Sketch MAE (ViT-H, 448) Top-1 accuracy 50.9 # 5
Image Classification iNaturalist MAE (ViT-H, 448) Top 1 Accuracy 83.4 # 1
Image Classification iNaturalist 2018 MAE (ViT-H, 448) Top-1 Accuracy 86.8% # 2
Image Classification iNaturalist 2019 MAE (ViT-H, 448) Top-1 Accuracy 88.3 # 1
Image Classification OmniBenchmark MAE Average Top-1 Accuracy 30.6 # 22
Image Classification Places205 MAE (ViT-H, 448) Top 1 Accuracy 66.8 # 4
Image Classification Places365-Standard MAE (ViT-H, 448) Top 1 Accuracy 60.3 # 2

Methods