arXiv AI
Jun 30
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their efficiency, as they operate directly on pre-trained networks without requiring retraining.
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arXiv AI
Jul 15
Sparse Autoencoders for Interpretable Out-of-Distribution Detection
arXiv:2607. 12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models.
arXiv Machine Learning
Jun 9
phepy: Visual benchmarks and improvements for out-of-distribution detectors
arXiv:2503. 05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain.
arXiv AI
Jul 8
Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics
arXiv:2510. 06505v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness of machine learning systems deployed in real-world applications.
arXiv Machine Learning
Jul 21
AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels
arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.
arXiv AI
Jun 2
CalArena: A Large-Scale Post-Hoc Calibration Benchmark
arXiv:2605. 30188v2 Announce Type: replace-cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated.
arXiv Machine Learning
Jun 9
Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
arXiv:2606. 07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.
arXiv Machine Learning
Jul 15
Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Model
arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.
arXiv Machine Learning
Jun 30
Bandwidth Selection in Kernel Density Estimation for Model Calibration
arXiv:2606. 29925v1 Announce Type: new Abstract: As deep learning models are increasingly deployed in high-stakes applications, providing well-calibrated uncertainty estimates has become as critical as achieving high predictive accuracy.
arXiv Machine Learning
Jun 26
At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
arXiv:2606. 26396v1 Announce Type: new Abstract: Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data.
arXiv Machine Learning
Aug 11
An Information-Theoretic Framework for Feature Construction in Out-of-Distribution Detection
arXiv:2506. 14194v2 Announce Type: replace Abstract: We present a theory for the construction of out-of-distribution (OOD) detection features for neural networks.
Hugging Face Trending Papers
Jun 29
Bandwidth Selection in Kernel Density Estimation for Model Calibration
As deep learning models are increasingly deployed in high-stakes applications, providing well-calibrated uncertainty estimates has become as critical as achieving high predictive accuracy. While Kernel Density Estimation (KDE) has emerged as a smooth and continuous alternative to traditional binning for quantifying miscalibration, its reliability is heavily dependent on the choice of the kernel bandwidth.