OCO mitigates simplicity bias in OOD detection by predicting disentangled representations, dividing patterns into three co-occurrence scenarios from ID data, and applying divide-and-conquer detection.
Decoupled weight decay regularization
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PRADA uses probability ratios of autoregressive token sequences to detect and attribute images to specific generative models.
MultiModalPFN extends TabPFN with modality projectors, a multi-head gated MLP, and cross-attention pooler to unify tabular and non-tabular inputs, outperforming prior methods on medical and general multimodal datasets.
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Divide and Conquer: Object Co-occurrence Helps Mitigate Simplicity Bias in OOD Detection
OCO mitigates simplicity bias in OOD detection by predicting disentangled representations, dividing patterns into three co-occurrence scenarios from ID data, and applying divide-and-conquer detection.
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PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images
PRADA uses probability ratios of autoregressive token sequences to detect and attribute images to specific generative models.
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MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
MultiModalPFN extends TabPFN with modality projectors, a multi-head gated MLP, and cross-attention pooler to unify tabular and non-tabular inputs, outperforming prior methods on medical and general multimodal datasets.
- Concept-wise Attention for Fine-grained Concept Bottleneck Models