Results 11 to 20 of about 762,582 (351)
Inconsistency, Paraconsistency and ?-Inconsistency [PDF]
Principia: An International Journal of Epistemology, 2018 In this paper I’ll explore the relation between ?-inconsistency and plain inconsistency, in the context of theories that intend to capture semantic concepts. In particular, I’ll focus on two very well known inconsistent but non-trivial theories of truth:
Bruno Da Ré
doaj +5 more sources
GRADE guidance 36: updates to GRADE's approach to addressing inconsistency
Journal of Clinical Epidemiology, 2023 OBJECTIVES To update previous GRADE guidance addressing inconsistency and interpreting subgroup analyses. STUDY DESIGN AND SETTING Using an iterative process, we consulted with members of the GRADE working group through multiple rounds of written ...
Mónica Hultcrantz +1 more
exaly +2 more sources
Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies
Research Synthesis Methods, 2012 Meta‐analyses that simultaneously compare multiple treatments (usually referred to as network meta‐analyses or mixed treatment comparisons) are becoming increasingly common. An important component of a network meta‐analysis is an assessment of the extent
Julian P T Higgins +2 more
exaly +2 more sources
Artificial Intelligence, 2003 Nonmonotonic consequence is the subject of a vast literature, but the idea of a nonmonotonic counterpart of logical inconsistency—the idea of a defeasible property representing internal conflict of an inductive or evidential nature—has been entirely ...
Cross, Charles B.
core +3 more sources
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization [PDF]
Transactions of the Association for Computational Linguistics, 2021 In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection.
Philippe Laban +3 more
semanticscholar +1 more source
Pixel-Inconsistency Modeling for Image Manipulation Localization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Digital image forensics plays a crucial role in image authentication and manipulation localization. Despite the progress powered by deep neural networks, existing forgery localization methodologies exhibit limitations when deployed to unseen datasets and
Chenqi Kong +5 more
semanticscholar +1 more source
Spatiotemporal Inconsistency Learning for DeepFake Video Detection [PDF]
ACM Multimedia, 2021 The rapid development of facial manipulation techniques has aroused public concerns in recent years. Following the success of deep learning, existing methods always formulate DeepFake video detection as a binary classification problem and develop frame ...
Zhihao Gu +6 more
semanticscholar +1 more source
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection [PDF]
European Conference on Computer Vision, 2022 Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-level forged location annotations.
Wanyi Zhuang +7 more
semanticscholar +1 more source
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020 Graph-based models have been widely used to fraud detection tasks. Owing to the development of Graph Neural Networks~(GNNs), recent works have proposed many GNN-based fraud detectors based on either homogeneous or heterogeneous graphs.
Zhiwei Liu +4 more
semanticscholar +1 more source
Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation [PDF]
IEEE Transactions on Medical Imaging, 2021 In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty.
Yinghuan Shi +7 more
semanticscholar +1 more source