Out-of-Distribution Detection with Attention Head Masking for Multimodal
  Document Classification
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.
66c7bfcfa44fb37e208bc597
Christos Constantinou
66c7bfcfa44fb37e208bc598
Georgios Ioannides
66c7bfcfa44fb37e208bc599
Aman Chadha
claimed_verified
2024-08-26T07:15:54.948Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
66c7bfcfa44fb37e208bc59a
Aaron Elkins
extracted_pending
2024-08-22T22:46:42.624Z
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Aaron Elkins
user
jsbai-aaron
66c7bfcfa44fb37e208bc59b
Edwin Simpson
Out-of-Distribution Detection with Attention Head Masking for Multimodal
  Document Classification
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.
A novel attention head masking method achieves superior performance in multi-modal OOD detection for document classification, reducing false positive rates and introducing a new document dataset.
attention head masking (AHM) out-of-distribution (OOD) detection false positive rate (FPR) multi-modal documents document classification Transformer architecture