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Moving beyond just black and white towards full-colour variable palettes presents another set of problems. Taking the methods presented above and quantising an image using an arbitrary colour palette reveals that error-diffusion is also better at preserving colour information when compared to ordered dithering.
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Self-attention is required. The model must contain at least one self-attention layer. This is the defining feature of a transformer — without it, you have an MLP or RNN, not a transformer.
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Like the N-convex algorithm, this algorithm attempts to find a set of candidates whose centroid is close to . The key difference is that instead of taking unique candidates, we allow candidates to populate the set multiple times. The result is that the weight of each candidate is simply given by its frequency in the list, which we can then index by random selection:，详情可参考im钱包官方下载
Article InformationAuthor, 謝全恩（Osmond Chia）,