readme: ---
  tags:
  - few-shot learning
  - classification
  datasets:
  - mini-imagenet
  - HEp-2
  - BCCD-WBC
  - IKEA-FS
  - CVPRL2ID
  ---
  
  # MAE-FS
  
  Masked Autoencoders for Few-Shot Learning (MAE-FS) is a self-supervised, generative technique that reinforces few-shot classification performance for a prototypical backbone model. Given an embedded support set (produced by a frozen backbone), MAE-FS generates new prototypes, through a novel process, all of which are incorporated into class-based centroids. The reinforced centroids are used to classify unlabelled prototypes in the query set.
  
  For usage instructions and code, please see the Github repo of this work: https://github.com/Brikwerk/MAE-FS
  
  ## Model Date
  
  November 2022
  
  ## Model Type
  
  MAE-FS uses a self-attention Transformer encoder and decoder for its architecture. CONV4, ResNet-18, and DINO-S are used a backbone models to decompose images into embedded representations. All weights provided are named according to the backbone model included in the respective weights file.
  
  The following weights are provided:
  - CONV4.pth (uses a CONV4 backbone with an embedding dimension of 512)
  - CONV4_BASE.pth (uses a CONV4 backbone with an embedding dimension of 64)
  - RESNET18.pth (uses a ResNet-18 backbone with an embedding dimension of 512)
  - DINO.pth (uses a DINO-Small backbone with an embedding dimension of 384)