Hyperbolic Entailment Filtering for Improving Image-Text Contrastive Learning and Image-Only Self-Supervised Learning

Core Concepts

HYPE, a novel data filtering method, leverages hyperbolic embeddings and the concept of entailment cones to effectively extract modality-wise meaningful and well-aligned data from extensive, noisy image-text pair datasets, thereby enhancing the specificity and clarity of data semantics for improved model training.

Abstract

The paper introduces HYPE (HYPerbolic Entailment filtering), a novel data filtering methodology designed to address the challenges posed by the specificity and clarity of data semantics in self-supervised learning from large-scale, noisy image-text datasets.
Key highlights:

Existing CLIP-based filtering techniques focus solely on the alignment between images and texts, but fail to capture the specificity of individual data points.
HYPE leverages hyperbolic embeddings and the concept of entailment cones to evaluate and filter out samples with meaningless or underspecified semantics, improving the specificity of each data sample.
HYPE utilizes four metrics for filtering: image specificity (ϵi), text specificity (ϵt), negative Lorentzian distance (-dL), and CLIP cosine similarity (cos(θ)).
HYPE demonstrates significant improvements in filtering efficiency and sets a new state-of-the-art in the DataComp benchmark when combined with existing filtering techniques.
The image specificity (ϵi) can be independently applied to induce an image-only dataset from an image-text or image-only data pool, leading to superior performance in image-only self-supervised learning compared to CLIP-based filtering.

Stats

"The training dataset scale and quality are highly correlated to machine learning model performance."
"Carefully human-validated high-quality training data leads to better model performance than the same size of noisy data."
"Existing large datasets rely heavily on web-crawled documents by CommonCrawl, employing different heuristics for reducing the size of the dataset."
"CLIP-based filtering helps verify the semantic alignment between images and texts, but only considering alignment is not enough criterion for high-quality data filtering."

Quotes

"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."
"ϵi, also can be used independently to induce a dataset to train image-only models. We show that the dataset filtered by ϵi trains a better image-only self-supervised model than the alignment-based filtering."

Key Insights Distilled From

HYPE: Hyperbolic Entailment Filtering for Underspecified Images and Texts

by Wonjae Kim,S... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17507.pdf

Deeper Inquiries

How can HYPE be further extended to handle multimodal datasets beyond image-text, such as video-text or audio-text

To extend HYPE to handle multimodal datasets beyond image-text, such as video-text or audio-text, we can leverage the concept of hyperbolic embeddings in a similar manner. For video-text datasets, we can extract features from both the video frames and the corresponding text descriptions and map them into a hyperbolic space. By defining entailment cones for both modalities, we can measure the specificity of each modality and filter out samples with underspecified semantics. The entailment loss can be calculated between video frames and text descriptions to ensure alignment and specificity in the multimodal dataset. Similarly, for audio-text datasets, we can extract audio features and apply the same principles of hyperbolic embeddings and entailment cones to filter out noisy or underspecified samples.

What are the potential limitations of using hyperbolic embeddings for data filtering, and how can they be addressed

One potential limitation of using hyperbolic embeddings for data filtering is the computational complexity involved in training models with hyperbolic geometry. Hyperbolic spaces have non-trivial curvature, which can make optimization challenging and computationally expensive. To address this limitation, techniques such as efficient optimization algorithms tailored for hyperbolic spaces, model parallelism, or distributed training can be employed to improve training efficiency. Additionally, the interpretability of hyperbolic embeddings and entailment cones may pose challenges in understanding the filtering decisions made by the model. Providing explanations or visualizations of the filtering process can help mitigate this limitation and enhance the transparency of the filtering mechanism.

Could the principles of HYPE be applied to other areas of machine learning, such as reinforcement learning or natural language processing, to improve the quality of training data

The principles of HYPE can be applied to other areas of machine learning, such as reinforcement learning or natural language processing, to enhance the quality of training data. In reinforcement learning, HYPE can be used to filter out noisy or irrelevant state-action pairs, ensuring that the training data for the agent is specific and aligned with the task objectives. By incorporating specificity metrics and entailment cones, the agent can learn from more informative and relevant experiences, leading to improved learning efficiency and performance. In natural language processing, HYPE can aid in filtering out ambiguous or misleading text data, ensuring that language models are trained on high-quality and semantically rich textual inputs. This can result in more accurate language understanding and generation capabilities in NLP tasks.