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README.md

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+---
+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)