MacrOData supplies three large, curated benchmark suites totaling 2,446 datasets for tabular outlier detection, complete with standardized splits, metadata, and a public leaderboard.
author Zhou, K
6 Pith papers cite this work, alongside 422 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
A dual-axis taxonomy classifies image degradations by causal source and perceptual effect, with a severity quantification layer using standard quality metrics, demonstrated via a COCO-based object detector robustness benchmark.
Face-Trace attributes synthetic face images to known generators, rejects images from unseen generators via an energy score, and clusters the rejected images into groups corresponding to distinct unknown generators, including in an incremental setting.
RPC is a post-hoc calibration technique that augments flow-based anomaly scores with nearest-prototype deviation in the frozen latent space, gated by keypoint confidence, yielding consistent AUROC gains on video anomaly detection tasks.
Case study applies verifier-guided LLM evolutionary agents to contraction-order optimization in tensor networks and concludes that human validation remains essential.
citing papers explorer
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MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
MacrOData supplies three large, curated benchmark suites totaling 2,446 datasets for tabular outlier detection, complete with standardized splits, metadata, and a public leaderboard.
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OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
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A Causally Grounded Taxonomy for Image Degradation Robustness Evaluation
A dual-axis taxonomy classifies image degradations by causal source and perceptual effect, with a severity quantification layer using standard quality metrics, demonstrated via a COCO-based object detector robustness benchmark.
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Face-Trace: Open-Set Attribution and Progressive Discovery of Synthetic Face Generators
Face-Trace attributes synthetic face images to known generators, rejects images from unseen generators via an energy score, and clusters the rejected images into groups corresponding to distinct unknown generators, including in an incremental setting.
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Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection
RPC is a post-hoc calibration technique that augments flow-based anomaly scores with nearest-prototype deviation in the frozen latent space, gated by keypoint confidence, yielding consistent AUROC gains on video anomaly detection tasks.
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Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks
Case study applies verifier-guided LLM evolutionary agents to contraction-order optimization in tensor networks and concludes that human validation remains essential.