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Found 10 papers, 4 papers with code
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos.
AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media
To address the above challenges, we develop a deep learning based Analogy-aware Offensive Meme Detection (AOMD) framework to learn the implicit analogy from the multi-modal contents of the meme and effectively detect offensive analogy memes.
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time.
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
Additionally, we design an adversarial training method tailored for sequential recommender systems.
Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19
However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e. g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation.
Domain Adaptation for Question Answering via Question Classification
In this work, we investigate the potential benefits of question classification for QA domain adaptation.
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems.
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process.
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
As such, MetaAdapt can learn how to adapt the misinformation detection model and exploit the source data for improved performance in the target domain.
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
The proliferation of online misinformation has posed significant threats to public interest.
