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The paper identifies that reintroducing privileged context to a distilled student model degrades performance (context-induced degradation), and proposes a lightweight consistency regularizer that anchors no-context outputs to mitigate this issue, improving robustness across 12 configurations.
This paper proposes CAT, a cross-scale aligned transformer that enforces consistency between intermediate and final GAN outputs to resolve trajectory misalignment, achieving state-of-the-art FID of 1.56 on ImageNet-256.