A visualization protocol using unsupervised semantic segmentation outputs reveals positional biases, scaling behaviors, and boundary artifacts in self-supervised ViTs and distinguishes them from locality bias.
arXiv preprint arXiv:2404.16818 (2024)
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Unsupervised Semantic Segmentation Facilitates Model Understanding
A visualization protocol using unsupervised semantic segmentation outputs reveals positional biases, scaling behaviors, and boundary artifacts in self-supervised ViTs and distinguishes them from locality bias.