Include verification results
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.
。搜狗输入法下载对此有专业解读
另一方面，软件股的超卖现象也引发部分投资者关注抄底机会。一些机构认为，像微软这样的巨头仍有潜力在 AI 时代获益，但大多数中小型 SaaS 企业由于面临颠覆风险，其股价短期内波动幅度较大。市场分化明显，投资者需要区分 AI 领域的潜在赢家和输家。
Платон Щукин (Редактор отдела «Экономика»)