HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts
arxiv(2024)
摘要
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 ϵ_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.
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