AppRay integrates LLM-guided task-oriented exploration with a contrastive learning multi-label classifier and rule-based refiner to detect intra- and inter-page dark patterns, reporting 0.89/0.85 F1 on new datasets with large gains over prior methods.
A Survey on Contrastive Self- supervised Learning
3 Pith papers cite this work. Polarity classification is still indexing.
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OC-Distill combines ontology-aware contrastive pretraining with cross-modal distillation to improve ICU risk prediction performance and label efficiency while using only vital signs at inference.
Survey benchmarks SSL instance discrimination and masked image modeling for object detection, finding instance discrimination suits CNN encoders while MIM suits ViT encoders and custom pre-training, especially for small objects.
citing papers explorer
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From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
AppRay integrates LLM-guided task-oriented exploration with a contrastive learning multi-label classifier and rule-based refiner to detect intra- and inter-page dark patterns, reporting 0.89/0.85 F1 on new datasets with large gains over prior methods.
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OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
OC-Distill combines ontology-aware contrastive pretraining with cross-modal distillation to improve ICU risk prediction performance and label efficiency while using only vital signs at inference.
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Self-Supervised Learning for Real-World Object Detection: a Survey
Survey benchmarks SSL instance discrimination and masked image modeling for object detection, finding instance discrimination suits CNN encoders while MIM suits ViT encoders and custom pre-training, especially for small objects.