SG-OIF: A Stability-Guided Online Influence Framework for Reliable Vision Data

• URL: http://arxiv.org/abs/2511.19466v1
• Date: Fri, 21 Nov 2025 19:58:54 GMT
• Title: SG-OIF: A Stability-Guided Online Influence Framework for Reliable Vision Data
• Authors: Penghao Rao, Runmin Jiang, Min Xu,
• Abstract summary: In this paper, we introduce a Stability-Guided Online Influence Framework (SG-OIF) for Approximating training-point influence on test predictions.<n>We show that SG-OIF achieves 91.1% accuracy in the top 1% prediction samples on the CIFAR-10, and 99.8% AUPR score on MNIST.
• Score: 6.4391040754741296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Approximating training-point influence on test predictions is critical for deploying deep-learning vision models, essential for locating noisy data. Though the influence function was proposed for attributing how infinitesimal up-weighting or removal of individual training examples affects model outputs, its implementation is still challenging in deep-learning vision models: inverse-curvature computations are expensive, and training non-stationarity invalidates static approximations. Prior works use iterative solvers and low-rank surrogates to reduce cost, but offline computation lags behind training dynamics, and missing confidence calibration yields fragile rankings that misidentify critical examples. To address these challenges, we introduce a Stability-Guided Online Influence Framework (SG-OIF), the first framework that treats algorithmic stability as a real-time controller, which (i) maintains lightweight anchor IHVPs via stochastic Richardson and preconditioned Neumann; (ii) proposes modular curvature backends to modulate per-example influence scores using stability-guided residual thresholds, anomaly gating, and confidence. Experimental results show that SG-OIF achieves SOTA (State-Of-The-Art) on noise-label and out-of-distribution detection tasks across multiple datasets with various corruption. Notably, our approach achieves 91.1\% accuracy in the top 1\% prediction samples on the CIFAR-10 (20\% asym), and gets 99.8\% AUPR score on MNIST, effectively demonstrating that this framework is a practical controller for online influence estimation.

Related papers

• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• Learning to be Reproducible: Custom Loss Design for Robust Neural Networks [4.3094059981414405]
  We propose a Custom Loss Function (CLF) that balances predictive accuracy with training stability.<n>CLF significantly improves training without sacrificing predictive performance.<n>These results establish CLF as an effective and efficient strategy for developing more stable, reliable and trustworthy neural networks.
  arXiv  Detail & Related papers  (2026-01-02T05:31:08Z)
• Contrastive Knowledge Transfer and Robust Optimization for Secure Alignment of Large Language Models [9.353236468990945]
  This paper addresses the limitations of large-scale language models in safety alignment and robustness.<n>It proposes a fine-tuning method that combines contrastive distillation with noise-robust training.<n>Results show that the method significantly outperforms existing baselines in knowledge transfer, robustness, and overall safety.
  arXiv  Detail & Related papers  (2025-10-31T00:54:33Z)
• MaP: A Unified Framework for Reliable Evaluation of Pre-training Dynamics [72.00014675808228]
  Instability in Large Language Models evaluation process obscures true learning dynamics.<n>We introduce textbfMaP, a framework that integrates underlineMerging underlineand the underlinePass@k metric.<n>Experiments show that MaP yields significantly smoother performance curves, reduces inter-run variance, and ensures more consistent rankings.
  arXiv  Detail & Related papers  (2025-10-10T11:40:27Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• Feature Statistics with Uncertainty Help Adversarial Robustness [19.01087281157066]
  adversarial attacks tend to shift the distributions of feature statistics.<n>We propose an uncertainty-driven feature statistics adjustment module for robustness enhancement.<n>The proposed FSU module has universal applicability in training, attacking, predicting, and fine-tuning.
  arXiv  Detail & Related papers  (2025-03-26T14:30:33Z)
• Feasible Learning [78.6167929413604]
  We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample.<n>Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
  arXiv  Detail & Related papers  (2025-01-24T20:39:38Z)
• A Conformal Approach to Feature-based Newsvendor under Model Misspecification [2.801095519296785]
  We propose a model-free and distribution-free framework inspired by conformal prediction.<n>We validate our framework using both simulated data and a real-world dataset from the Capital Bikeshare program in Washington, D.C.
  arXiv  Detail & Related papers  (2024-12-17T18:34:43Z)
• Impact of Noisy Supervision in Foundation Model Learning [91.56591923244943]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets.<n>We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout [15.569507252445144]
  Label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning. We propose a simple yet effective framework by introducing a prior-calibrated softmax function for computing the cross-entropy loss. The improved model performance over existing baselines in the presence of non-IID data and client dropout is demonstrated.
  arXiv  Detail & Related papers  (2023-03-11T05:17:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.