image-clustering

Proposes a novel self-supervised image clustering framework that uses an evolution-strategy outer loop to maximize a 'surprise score' without needing a per-step loss, paired with a gradient-descent inner loop, achieving state-of-the-art results on standard benchmarks in the strict non-parametric setting.