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--- |
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tags: |
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- few-shot learning |
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- classification |
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datasets: |
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- mini-imagenet |
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- HEp-2 |
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- BCCD-WBC |
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- IKEA-FS |
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- CVPRL2ID |
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--- |
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# MAE-FS |
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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. |
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For usage instructions and code, please see the Github repo of this work: https://github.com/Brikwerk/MAE-FS |
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## Model Date |
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November 2022 |
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## Model Type |
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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. |
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The following weights are provided: |
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- CONV4.pth (uses a CONV4 backbone with an embedding dimension of 512) |
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- CONV4_BASE.pth (uses a CONV4 backbone with an embedding dimension of 64) |
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- RESNET18.pth (uses a ResNet-18 backbone with an embedding dimension of 512) |
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- DINO.pth (uses a DINO-Small backbone with an embedding dimension of 384) |
