The N-closest or N-best dithering algorithm is a straightforward solution to the N-candidate problem. As the name suggests, the set of candidates is given by the closest palette colours to the input pixel. To determine their weights, we simply take the inverse of the distance to the input pixel. This is essentially the inverse distance weighting (IDW) method for multivariate interpolation, also known as Shepard’s method. The following pseudocode sketches out a possible implementation:
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Magda Szubanski was inducted into the Logies Hall of Fame last year
以 DeepSeek 自己做的蒸馏尝试为例:基于隔壁千问蒸馏自家的 R1 模型后得到的 DeepSeek-R1-Distill-Qwen 1.5B 这个小模型,仅靠 7000 条样本和极低的计算成本,就在 AIME24 数学竞赛基准上超越了 OpenAI 的 o1-preview。
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