Summary of Out-of-distribution Detection with Attention Head Masking For Multimodal Document Classification, by Christos Constantinou et al.
Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification
by Christos Constantinou, Georgios Ioannides, Aman Chadha, Aaron Elkins, Edwin Simpson
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed attention head masking (AHM) methodology outperforms state-of-the-art approaches in detecting out-of-distribution data in multi-modal document classification systems. By leveraging the Transformer architecture, AHM effectively models visual and textual information from documents, demonstrating a significant decrease in false positive rate up to 7.5% compared to existing solutions. This novel approach addresses the lack of research on OOD detection for multi-modal inputs and showcases its generalizability to new data sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Out-of-distribution (OOD) data detection is important for machine learning applications because it helps prevent overconfident models that can lead to unreliable and unsafe systems. Most existing methods focus on single-type inputs like images or text, but there’s a need for OOD detection in multi-modal documents. A new method called attention head masking (AHM) works well for this task. It uses the Transformer architecture to analyze both visual and textual information from documents. AHM is better than other approaches at finding OOD data, with fewer false positives. This method can be used in many areas where document classification is important. |
Keywords
» Artificial intelligence » Attention » Classification » Machine learning » Multi modal » Transformer
