在人工智能领域，自监督学习（SSL）正逐渐成为研究的热点，尤其是在视觉表征学习方面。近来，Yann LeCun和谢赛宁等研究者的最新成果，颠覆了我们对视觉自监督学习的传统认知。其研究表明，无语言的视觉表征学习能够在多模态任务中与目前业界领先的CLIP模型相抗衡，甚至在某些场景下超越之。

1. LeCun等研究人员通过新模型Web-SSL验证了SSL在多模态任务中的潜力，证明其在扩展模型和数据规模后，能媲美甚至超越CLIP。 2.

IT之家 4 月 14 日消息，SSL / TLS 证书是一种用于网站的安全协议，苹果此前向 CA / B 论坛 (负责管理 SSL / TLS 证书的行业组织) 提议将所有证书有效期缩短至 45 天。

在计算机视觉和自然语言处理的交汇处，视觉自监督学习（SSL）逐渐成为了当今学术界关注的焦点。最近，一项由知名研究者Yann LeCun、谢赛宁等人共同完成的研究引发了广泛的讨论。他们发布的论文《Scaling Language-Free Visual Representation Learning》通过实证研究表明，视觉SSL终于在视觉问题解答（VQA）任务上追赶上了语言图像预训练（CLIP），逆 ...

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