title: Out-of-Distribution Detection with Attention Head Masking for Multimodal
    Document Classification
summary: Detecting out-of-distribution (OOD) data is crucial in machine learning
  applications to mitigate the risk of model overconfidence, thereby enhancing
  the reliability and safety of deployed systems. The majority of existing OOD
  detection methods predominantly address uni-modal inputs, such as images or
  texts. In the context of multi-modal documents, there is a notable lack of
  extensive research on the performance of these methods, which have primarily
  been developed with a focus on computer vision tasks. We propose a novel
  methodology termed as attention head masking (AHM) for multi-modal OOD tasks in
  document classification systems. Our empirical results demonstrate that the
  proposed AHM method outperforms all state-of-the-art approaches and
  significantly decreases the false positive rate (FPR) compared to existing
  solutions up to 7.5\%. This methodology generalizes well to multi-modal data,
  such as documents, where visual and textual information are modeled under the
  same Transformer architecture. To address the scarcity of high-quality publicly
  available document datasets and encourage further research on OOD detection for
  documents, we introduce FinanceDocs, a new document AI dataset. Our code and
  dataset are publicly available.
paper_authors._id: 66c7bfcfa44fb37e208bc597
paper_authors.name: Christos Constantinou
paper_authors._id: 66c7bfcfa44fb37e208bc598
paper_authors.name: Georgios Ioannides
paper_authors._id: 66c7bfcfa44fb37e208bc599
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-26T07:15:54.948Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 66c7bfcfa44fb37e208bc59a
paper_authors.name: Aaron Elkins
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-08-22T22:46:42.624Z
paper_authors._id: 6615cf1a0ba998609c977de4
paper_authors.avatarUrl: /avatars/42bd9bc14302976fee87adc04f3dbdc5.svg
paper_authors.fullname: Aaron Elkins
paper_authors.type: user
paper_authors.user: jsbai-aaron
paper_authors._id: 66c7bfcfa44fb37e208bc59b
paper_authors.name: Edwin Simpson
paper_title: Out-of-Distribution Detection with Attention Head Masking for Multimodal
    Document Classification
paper_summary: Detecting out-of-distribution (OOD) data is crucial in machine learning
  applications to mitigate the risk of model overconfidence, thereby enhancing
  the reliability and safety of deployed systems. The majority of existing OOD
  detection methods predominantly address uni-modal inputs, such as images or
  texts. In the context of multi-modal documents, there is a notable lack of
  extensive research on the performance of these methods, which have primarily
  been developed with a focus on computer vision tasks. We propose a novel
  methodology termed as attention head masking (AHM) for multi-modal OOD tasks in
  document classification systems. Our empirical results demonstrate that the
  proposed AHM method outperforms all state-of-the-art approaches and
  significantly decreases the false positive rate (FPR) compared to existing
  solutions up to 7.5\%. This methodology generalizes well to multi-modal data,
  such as documents, where visual and textual information are modeled under the
  same Transformer architecture. To address the scarcity of high-quality publicly
  available document datasets and encourage further research on OOD detection for
  documents, we introduce FinanceDocs, a new document AI dataset. Our code and
  dataset are publicly available.
paper_ai_keywords: attention head masking (AHM) out-of-distribution (OOD) detection text methodology multi-modal documents document classification Transformer architecture financeDocs false positive rate (FPR)