Learning Domain-Invariant Features for Out-of-Context News Detection
- URL: http://arxiv.org/abs/2406.07430v1
- Date: Tue, 11 Jun 2024 16:34:02 GMT
- Title: Learning Domain-Invariant Features for Out-of-Context News Detection
- Authors: Yimeng Gu, Mengqi Zhang, Ignacio Castro, Shu Wu, Gareth Tyson,
- Abstract summary: Multimodal out-of-context news is a common type of misinformation on online media platforms.
In this work, we focus on domain adaptive out-of-context news detection.
We propose ConDA-TTA which applies contrastive learning and maximum mean discrepancy (MMD) to learn the domain-invariant feature.
- Score: 19.335065976085982
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multimodal out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside an invalid out-of-context news image. Reflecting its importance, researchers have developed models to detect such misinformation. However, a common limitation of these models is that they only consider the scenario where pre-labeled data is available for each domain, failing to address the out-of-context news detection on unlabeled domains (e.g., unverified news on new topics or agencies). In this work, we therefore focus on domain adaptive out-of-context news detection. In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time Adaptation) which applies contrastive learning and maximum mean discrepancy (MMD) to learn the domain-invariant feature. In addition, it leverages target domain statistics during test-time to further assist domain adaptation. Experimental results show that our approach outperforms baselines in 5 out of 7 domain adaptation settings on two public datasets, by as much as 2.93% in F1 and 2.08% in accuracy.
Related papers
- DPOD: Domain-Specific Prompt Tuning for Multimodal Fake News Detection [15.599951180606947]
Fake news using out-of-context images has become widespread and is a relevant problem in this era of information overload.
We explore whether out-of-domain data can help to improve out-of-context misinformation detection of a desired domain.
We propose a novel framework termed DPOD (Domain-specific Prompt-tuning using Out-of-Domain data)
arXiv Detail & Related papers (2023-11-27T08:49:26Z) - Robust Domain Misinformation Detection via Multi-modal Feature Alignment [49.89164555394584]
We propose a robust domain and cross-modal approach for multi-modal misinformation detection.
It reduces the domain shift by aligning the joint distribution of textual and visual modalities.
We also propose a framework that simultaneously considers application scenarios of domain generalization.
arXiv Detail & Related papers (2023-11-24T07:06:16Z) - SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation [62.889835139583965]
We introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data.
As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data.
Our experiments demonstrate that our method achieves a better performance than the current state of the art, both in real-to-real and synthetic-to-real scenarios.
arXiv Detail & Related papers (2023-04-06T17:36:23Z) - Improving Fake News Detection of Influential Domain via Domain- and
Instance-Level Transfer [16.886024206337257]
We propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND)
DITFEND could improve the performance of specific target domains.
Online experiments show that it brings additional improvements over the base models in a real-world scenario.
arXiv Detail & Related papers (2022-09-19T10:21:13Z) - Deep Unsupervised Domain Adaptation: A Review of Recent Advances and
Perspectives [16.68091981866261]
Unsupervised domain adaptation (UDA) is proposed to counter the performance drop on data in a target domain.
UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc.
arXiv Detail & Related papers (2022-08-15T20:05:07Z) - Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised
Pre-Training [67.71228426496013]
We show that using target domain data during pre-training leads to large performance improvements across a variety of setups.
We find that pre-training on multiple domains improves performance generalization on domains not seen during training.
arXiv Detail & Related papers (2021-04-02T12:53:15Z) - Effective Label Propagation for Discriminative Semi-Supervised Domain
Adaptation [76.41664929948607]
Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks.
We present a novel and effective method to tackle this problem by using effective inter-domain and intra-domain semantic information propagation.
Our source code and pre-trained models will be released soon.
arXiv Detail & Related papers (2020-12-04T14:28:19Z) - Domain Adaptation with Incomplete Target Domains [61.68950959231601]
We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge.
In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain.
We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains.
arXiv Detail & Related papers (2020-12-03T00:07:40Z) - A Brief Review of Domain Adaptation [1.2043574473965317]
This paper focuses on unsupervised domain adaptation, where the labels are only available in the source domain.
It presents some successful shallow and deep domain adaptation approaches that aim to deal with domain adaptation problems.
arXiv Detail & Related papers (2020-10-07T07:05:32Z) - Domain Adaptation for Semantic Parsing [68.81787666086554]
We propose a novel semantic for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain.
Our semantic benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages.
Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies.
arXiv Detail & Related papers (2020-06-23T14:47:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.