In an era where the volume of data drives the effectiveness of
self-supervised learning, the specificity and clarity of data semantics play a
crucial role in model training. Addressing this, we introduce HYPerbolic
Entailment filtering (HYPE), a novel methodology designed to meticulously
extract modality-wise meaningful and well-aligned data from extensive, noisy
image-text pair datasets. Our approach leverages hyperbolic embeddings and the
concept of entailment cones to evaluate and filter out samples with meaningless
or underspecified semantics, focusing on enhancing the specificity of each data
sample. HYPE not only demonstrates a significant improvement in filtering
efficiency but also sets a new state-of-the-art in the DataComp benchmark when
combined with existing filtering techniques. This breakthrough showcases the
potential of HYPE to refine the data selection process, thereby contributing to
the development of more accurate and efficient self-supervised learning models.
Additionally, the image specificity $\epsilon_{i}$ can be independently applied
to induce an image-only dataset from an image-text or image-only data pool for
training image-only self-supervised models and showed superior performance when
compared to the dataset induced by CLIP score.

本研究介绍了一种名为 HYPerbolic Entailment filtering (HYPE) 的新方法，利用超伸缩嵌入和蕴含锥的概念，精确提取具有明确语义的图像 - 文本对数据集，以增强数据样本的特异性。HYPE 在数据筛选效率方面取得了显著的改进，并在与现有筛选技术相结合时创下了 DataComp 基准的最新记录。该研究突破了数据选择过程的潜力，为更准确高效的自监督学习模型的发展做出了贡献。