Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
2 Pith papers cite this work, alongside 343 external citations. Polarity classification is still indexing.
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abstract
Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research.
years
2026 2representative citing papers
A five-layer, 20-signal Trust Margin metric and Exponential-smoothing prediction engine achieve 8-hour-ahead degradation forecasting for production LLM agent systems with MAE=1.228 and 76.8% direction accuracy.
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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Agent Delivery Engineering Predictive Reliability Framework
A five-layer, 20-signal Trust Margin metric and Exponential-smoothing prediction engine achieve 8-hour-ahead degradation forecasting for production LLM agent systems with MAE=1.228 and 76.8% direction accuracy.