Masking principal components instead of pixel patches in masked autoencoders yields better image classification representations across CIFAR10, TinyImageNet, and three MedMNIST datasets.
We use the linear lr scaling rule: lr = base lr×batchsize / 256 (Goyal et al., 2017)
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From Pixels to Components: Eigenvector Masking for Visual Representation Learning
Masking principal components instead of pixel patches in masked autoencoders yields better image classification representations across CIFAR10, TinyImageNet, and three MedMNIST datasets.