REVIEW 8 cited by
VOS: Learning What You Don't Know by Virtual Outlier Synthesis
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Previous approaches rely on real outlier datasets for model regularization, which can be costly and sometimes infeasible to obtain in practice. In this paper, we present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers that can meaningfully regularize the model's decision boundary during training. Specifically, VOS samples virtual outliers from the low-likelihood region of the class-conditional distribution estimated in the feature space. Alongside, we introduce a novel unknown-aware training objective, which contrastively shapes the uncertainty space between the ID data and synthesized outlier data. VOS achieves competitive performance on both object detection and image classification models, reducing the FPR95 by up to 9.36% compared to the previous best method on object detectors. Code is available at https://github.com/deeplearning-wisc/vos.
Forward citations
Cited by 8 Pith papers
-
Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection
SynOOD generates synthetic near-boundary OOD images with MLLM-guided inpainting and energy-score gradients, then fine-tunes CLIP image and text features, reporting state-of-the-art OOD detection on ImageNet benchmarks.
-
Gradient Short-Circuit: Efficient Out-of-Distribution Detection via Feature Intervention
Gradient Short-Circuit masks the top-gradient feature coordinates, approximates the resulting logits with a first-order Taylor step, and reports large FPR95 improvements on standard OOD benchmarks.
-
Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes?
Inpainting unusual objects into street scenes reveals that open-vocabulary detectors miss objects based on image location rather than object semantics.
-
Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation
Starting diffusion sampling from variance-boosted noise and skipping early timesteps generates minority samples at guided-method quality with far less compute.
-
The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model
CLIP embeddings are modeled as a mixture of von Mises-Fisher distributions on the unit sphere, improving out-of-distribution detection and semantic decomposition over single-Gaussian baselines.
-
$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization
ΔEnergy, an energy-change OOD score for CLIP, and its EBM fine-tuning loss simultaneously improve OOD detection and covariate-shift generalization.
-
Realistic Evaluation of TabPFN v2 in Open Environments
TabPFN v2 underperforms tree-based models on most open-environment tabular tasks and is only preferable on small, covariate-shifted, class-balanced data.
-
LLM-Guided Agentic Object Detection for Open-World Understanding
An LLM generates scene-specific object names that are fed to YOLO-World, enabling label-free open-world detection evaluated with new CAAP and SNAP metrics.
Discussion (0). Continue with ORCID to comment.