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Google AI Propose LANISTR: An Attention-based Machine Learning Framework to Learn from Language, Image, and Structured Data

Google AI Propose LANISTR: An Attention-based Machine Learning Framework to Learn from Language, Image, and Structured Data

Google AI Propose LANISTR: An Attention-based Machine Learning Framework to Learn from Language, Image, and Structured Data

Google Cloud AI Researchers have introduced LANISTR to address the challenges of effectively and efficiently handling unstructured and structured data within a framework. In machine learning, handling multimodal data—comprising language, images, and structured data—is increasingly crucial. The key challenge is the issue of missing modalities in large-scale, unlabeled, and structured data like tables and time series. Traditional methods often struggle when one or more types of data are absent, leading to suboptimal model performance.

Practical Solutions and Value

  • LANISTR leverages unimodal and multimodal masking strategies to create a robust pretraining objective that can handle missing modalities effectively.
  • The framework improves the adaptability and generalizability of multimodal models in scenarios with limited labeled data.
  • LANISTR shows effectiveness in out-of-distribution scenarios and achieves significant gains in accuracy and generalization, even with the availability of labeled data.

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