PRADA uses probability ratios of autoregressive token sequences to detect and attribute images to specific generative models.
Decoupled weight decay regularization
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
OCO uses object co-occurrence analysis to divide OOD detection into scenarios based on ID training data patterns for improved near-OOD performance.
citing papers explorer
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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.
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Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis
OCO uses object co-occurrence analysis to divide OOD detection into scenarios based on ID training data patterns for improved near-OOD performance.
- Concept-wise Attention for Fine-grained Concept Bottleneck Models