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GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
The bigger they come, the slower they move
荣耀做magic AI手机时，当时手机市场陷入了堆参数堆电池的怪圈。赵明则坚定表示不跟风参数竞赛。他的逻辑是“端侧AI是个人工具，任务是让用户变强，不是让参数变高。”