Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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9 Pith papers cite this work, alongside 9,744 external citations. Polarity classification is still indexing.
9
Pith papers citing it
9,744
external citations · OpenAlex
representative citing papers
Under DC-only transfer attacks, LLM IDS vulnerability is substantial but dataset- and comparator-dependent, with gradient/score attacks transferring better than greedy ones.
SEAGAN applies a domain-specific graph attention network to classify limitation states in A-Ci curves, achieving F1-score 0.857 and accuracy 0.882 on synthetic data with known ground truth.
After correcting prior flaws, a class-dependent hybrid augmentation strategy plus clinical subtype aggregation raises average macro-F1 robustness across eight classifiers on a 400-patient seven-subtype migraine dataset, with peak 0.914 under proportional growth.
Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
A conditional graph neural network serves as an accurate and fast surrogate for semi-analytic galaxy formation models, predicting key properties across cosmic time.
Targeted data augmentation with GPT-4 synthetic responses and ALP phrase-level extraction substantially improves SciBERT performance on severely imbalanced rubric categories for NGSS scientific explanations, achieving perfect precision/recall/F1 on several categories while outperforming SMOTE.
On imbalanced MIMIC-IV-ED and eICU tabular tasks, XGBoost and other tree ensembles are more robust and scalable than deep tabular models, although the paper's abstract and body disagree about which models were evaluated.
A heterogeneous ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base with independent task modeling and class weighting is reported as effective for multilingual, multicultural, and multievent online polarization detection.
citing papers explorer
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers
Under DC-only transfer attacks, LLM IDS vulnerability is substantial but dataset- and comparator-dependent, with gradient/score attacks transferring better than greedy ones.
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SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes
SEAGAN applies a domain-specific graph attention network to classify limitation states in A-Ci curves, achieving F1-score 0.857 and accuracy 0.882 on synthetic data with known ground truth.
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Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
After correcting prior flaws, a class-dependent hybrid augmentation strategy plus clinical subtype aggregation raises average macro-F1 robustness across eight classifiers on a 400-patient seven-subtype migraine dataset, with peak 0.914 under proportional growth.
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Disentangling Sampling from Training Budget in Class-Imbalanced CT Body Composition Segmentation
Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
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A graph-based Neural Network surrogate model for accelerating semi-analytical model of galaxy formation and evolution
A conditional graph neural network serves as an accurate and fast surrogate for semi-analytic galaxy formation models, predicting key properties across cosmic time.
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Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
Targeted data augmentation with GPT-4 synthetic responses and ALP phrase-level extraction substantially improves SciBERT performance on severely imbalanced rubric categories for NGSS scientific explanations, achieving perfect precision/recall/F1 on several categories while outperforming SMOTE.
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An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care
On imbalanced MIMIC-IV-ED and eICU tabular tasks, XGBoost and other tree ensembles are more robust and scalable than deep tabular models, although the paper's abstract and body disagree about which models were evaluated.
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YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
A heterogeneous ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base with independent task modeling and class weighting is reported as effective for multilingual, multicultural, and multievent online polarization detection.