A hybrid evolution-strategy and gradient-descent framework maximizes a non-differentiable 'surprise score' to discover non-random features for non-parametric self-supervised image clustering.
Self-supervised learn- ing from images with a joint-embedding predictive architecture
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Converge to Surprise: Evolutionary Self-supervised Image Clustering
A hybrid evolution-strategy and gradient-descent framework maximizes a non-differentiable 'surprise score' to discover non-random features for non-parametric self-supervised image clustering.