The paper introduces a leakage‑free 3×3 spatio‑temporal partition for evaluating inductive kriging, ensuring training, validation, and testing occur on distinct spatial and temporal domains. It proposes DRIK, a framework that includes Spatial Continuity Regularization, Masked Flow Disambiguation, and Structural Domain Expansion to mitigate structural shifts from unseen nodes. Experiments on six datasets show DRIK outperforms existing baselines, reducing MAE by up to 12.48% and achieving lower test‑to‑validation MAE ratios under the stricter evaluation protocol.
By Chen Yang, Changhao Zhao, Haoyang Zhao, Youquan He, Chen Wang, Jiansheng Fan
SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.
By Yiming Luo, Rongqiang Zhao, Jie Liu
arXiv:2609.39681v1 Announce Type: new
Abstract: Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g...
By Ivan Martinovi\'c, Josip \v{S}ari\'c, Yuki M. Asano, Sini\v{s}a \v{S}egvi\'c
arXiv:2602.06924v3 Announce Type: replace
Abstract: Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real-world settings li...
By Abinitha Gourabathina, Hyewon Jeong, Teya Bergamaschi, Marzyeh Ghassemi, Collin Stultz
arXiv:2608. 00073v1 Announce Type: cross Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging.
By Qinghui Liu, Jon Andr\'e Ottesen, Atle Bj{\o}rnerud, Kyrre Eeg Emblem
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.
By Eug\`ene Berta, David Holzm\"uller, Francis Bach, Michael I. Jordan