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.

翻译：在数据规模驱动自监督学习有效性的时代，数据语义的特异性与清晰度对模型训练起着关键作用。针对这一需求，我们提出双曲蕴含过滤（HYPerbolic Entailment filtering，HYPE）——一种旨在从大规模含噪图文对数据集中精细提取模态层面语义清晰且对齐良好的数据的新型方法论。本方法利用双曲嵌入与蕴含锥概念，评估并过滤含有无意义或欠指定语义的样本，聚焦于提升每个数据样本的特异性。HYPE不仅显著提升了过滤效率，在与现有过滤技术结合时，更在DataComp基准测试中确立了新的最优结果。这一突破揭示了HYPE在优化数据筛选流程方面的潜力，从而助力开发更精确高效的自监督学习模型。此外，图像特异性指标ε_i可独立应用于从图文对或纯图像数据池中提取纯图像数据集，用于训练纯图像自监督模型，且相比基于CLIP评分筛选的数据集展现出更优性能。