GEODE uses per-sample cosine-similarity scaling in a norm loss to preserve feature geometry for universal scorer-compatible OOD detection, matching or exceeding OE performance on CIFAR benchmarks.
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Learning Confidence for Out-of-Distribution Detection in Neural Networks
12 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safely perform. To jointly address these issues, we propose a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs. We demonstrate that on the task of out-of-distribution detection, our technique surpasses recently proposed techniques which construct confidence based on the network's output distribution, without requiring any additional labels or access to out-of-distribution examples. Additionally, we address the problem of calibrating out-of-distribution detectors, where we demonstrate that misclassified in-distribution examples can be used as a proxy for out-of-distribution examples.
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PROBE recasts MLIP uncertainty quantification as selective classification by training a compact discriminative classifier on frozen per-atom backbone embeddings, yielding a reliability probability that tracks actual error better than ensemble disagreement.
HealthPoint represents clinical events as points in a 4D space (content, time, modality, case) and applies low-rank relational attention to achieve state-of-the-art mortality prediction from multi-level incomplete multimodal EHRs.
Fold is a post-hoc OOD detector that exploits larger feature-Hessian curvature on OOD inputs together with partial feature normalization and a self-supervised AutoFold calibration scheme.
Introduces an architecture-agnostic Adapter Head and Invascal self-calibration objective to produce calibrated evidential uncertainty estimates for LiDAR range-view semantic segmentation while preserving accuracy.
PVLM combines parsing-aware vision-language modeling with dynamic contrastive learning to enable fine-grained zero-shot attribution of deepfakes to unseen generators and outperforms prior methods on a new benchmark.
CoOD decomposes inputs into components and applies Component Shift Score plus Compositional Consistency Score to improve detection of both standard and compositional out-of-distribution data.
Uncertainty optimization alone misses most safety gains; a decision-stage deferral policy removes up to 80% segmentation errors at 25% pixel deferral with cross-dataset robustness, while calibration does not improve decision quality.
RankOOD detects out-of-distribution samples by training a model to predict fixed class-specific ranking permutations via the Plackett-Luce loss, achieving a 4.3% FPR95 reduction on near-OOD TinyImageNet.
Benchmark across architectures and shift regimes finds OOD detector rankings shift with representation collapse; proposes NC-based shortlist predictor and PCA filter without extra OOD data.
An optimization-based deep learning pipeline selects informative patches from H&E whole-slide images to classify breast cancer into PAM50 subtypes, achieving F1 scores of 0.88 internally and 0.80 externally.
A multi-head RNN framework with learned confidence, ensemble uncertainty, auxiliary predictions, distance analysis, and diagnostics produces calibrated trust scores for NOx prediction, reducing MAE from 0.202 to 0.070 on the top 10% confidence subset.
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Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation
Introduces an architecture-agnostic Adapter Head and Invascal self-calibration objective to produce calibrated evidential uncertainty estimates for LiDAR range-view semantic segmentation while preserving accuracy.