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Gaze estimation models can now achieve comparable accuracy with 80-95% less labeled data, thanks to a semi-supervised approach that disentangles gaze components and learns robust representations via contrastive learning.
Achieve state-of-the-art sparse-view CT reconstruction with a diffusion model that generates visually consistent textures using fewer sampling steps, mitigating inherent randomness.
