Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
In search of lost domain generalization.arXiv preprint arXiv:2007.01434
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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GS-DIFF detects changes in 3D Gaussian Splatting scenes by direct primitive attribute comparison with anisotropic drift models and observability terms, outperforming render-then-compare baselines by ~17% mIoU.
A large-scale benchmark finds that recent multimodal domain generalization methods give only marginal gains over a plain ERM baseline, with no method winning consistently and all degrading sharply under corruption or missing modalities.
A cross-population framework for EEG Parkinson's detection using exhaustive 75 directional evaluations and nested validation shows asymmetric transfer and accuracy up to 94.1% when training diversity increases, supported by mixture risk theory.
Inter-laboratory measurement variance dominates the generalization gap in PROTAC activity prediction, capping LOTO AUROC near 0.67 across models and architectures.
Domain generalization via gradient reversal on the BreaKHis dataset produces magnification-invariant histopathology classifiers with three-fold smaller sparse embeddings and near-perfect cross-magnification signature reproducibility.
A cross-machine anomaly detection framework disentangles MOMENT embeddings using random forests to create machine-invariant condition features that improve generalization to unseen machines on industrial data.
citing papers explorer
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
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From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting
GS-DIFF detects changes in 3D Gaussian Splatting scenes by direct primitive attribute comparison with anisotropic drift models and observability terms, outperforming render-then-compare baselines by ~17% mIoU.
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Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study
A large-scale benchmark finds that recent multimodal domain generalization methods give only marginal gains over a plain ERM baseline, with no method winning consistently and all degrading sharply under corruption or missing modalities.
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Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection
A cross-population framework for EEG Parkinson's detection using exhaustive 75 directional evaluations and nested validation shows asymmetric transfer and accuracy up to 94.1% when training diversity increases, supported by mixture risk theory.
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Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling
Inter-laboratory measurement variance dominates the generalization gap in PROTAC activity prediction, capping LOTO AUROC near 0.67 across models and architectures.
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Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures
Domain generalization via gradient reversal on the BreaKHis dataset produces magnification-invariant histopathology classifiers with three-fold smaller sparse embeddings and near-perfect cross-magnification signature reproducibility.
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Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model
A cross-machine anomaly detection framework disentangles MOMENT embeddings using random forests to create machine-invariant condition features that improve generalization to unseen machines on industrial data.