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Henan University

Academic institutionasia · cn
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Research library15linked papers
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Selected work

Representative Papers

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

Jul 14, 2026

This work addresses the common issue in medical image segmentation where insufficient geometric priors lead to poor boundary representation. To this end, it introduces mean curvature as a geometric constraint into deep active contour models for the first time, formulating a novel loss function to enhance geometric consistency of segmentation boundaries. A lightweight convolutional kernel is employed to approximate mean curvature efficiently, significantly reducing computational overhead. Furthermore, the method integrates the Chan–Vese model with convolutional neural networks to jointly optimize region and boundary information. Evaluated on liver and spleen datasets, the proposed approach achieves state-of-the-art performance, markedly improving both segmentation accuracy and geometric plausibility.

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Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training

May 28, 2026

This work addresses the limited exploration of gradient perturbations during backpropagation in existing training methods, which often focus solely on forward perturbations and lack mechanisms for class-aware adaptive optimization. To bridge this gap, we establish a unified theoretical framework for gradient perturbation that subsumes techniques such as Sharpness-Aware Minimization (SAM) and gradient clipping. Within this framework, we propose Label-aware Perturbed Gradients (LPG), a novel method that adaptively modulates gradient norms to induce class-conditional augmentation effects. Leveraging PAC-Bayesian analysis, we theoretically connect the magnitude of gradient perturbations to generalization performance. LPG is designed as a modular, plug-and-play component and demonstrates consistent superiority over existing approaches across balanced classification, long-tailed learning, and noisy-label settings, while seamlessly integrating with other training strategies.

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Learning to Perturb Hidden Representations for Generalizable Deep Learning

May 28, 2026

Existing deep learning approaches lack a unified and effective mechanism for perturbing hidden-layer activations, limiting their ability to systematically enhance model generalization. This work proposes a class-aware, learnable perturbation framework for activations (LPA), which, for the first time, systematically analyzes the underlying mechanisms of hidden activation perturbation and reveals the opposing effects of expansive versus contractive perturbations on generalization. By leveraging projected gradient descent (PGD) optimization, LPA enables class-adaptive perturbations and theoretically connects them to flat minima and inter-layer amplification effects. Extensive experiments demonstrate that LPA significantly outperforms current methods across balanced classification, long-tailed learning, and domain generalization tasks, and further exhibits complementary gains when combined with logit-level perturbation techniques such as LPL.

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Recent publications

Latest Papers

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

Jul 14, 2026

This work addresses the common issue in medical image segmentation where insufficient geometric priors lead to poor boundary representation. To this end, it introduces mean curvature as a geometric constraint into deep active contour models for the first time, formulating a novel loss function to enhance geometric consistency of segmentation boundaries. A lightweight convolutional kernel is employed to approximate mean curvature efficiently, significantly reducing computational overhead. Furthermore, the method integrates the Chan–Vese model with convolutional neural networks to jointly optimize region and boundary information. Evaluated on liver and spleen datasets, the proposed approach achieves state-of-the-art performance, markedly improving both segmentation accuracy and geometric plausibility.

0 citationsRead paper

Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training

May 28, 2026

This work addresses the limited exploration of gradient perturbations during backpropagation in existing training methods, which often focus solely on forward perturbations and lack mechanisms for class-aware adaptive optimization. To bridge this gap, we establish a unified theoretical framework for gradient perturbation that subsumes techniques such as Sharpness-Aware Minimization (SAM) and gradient clipping. Within this framework, we propose Label-aware Perturbed Gradients (LPG), a novel method that adaptively modulates gradient norms to induce class-conditional augmentation effects. Leveraging PAC-Bayesian analysis, we theoretically connect the magnitude of gradient perturbations to generalization performance. LPG is designed as a modular, plug-and-play component and demonstrates consistent superiority over existing approaches across balanced classification, long-tailed learning, and noisy-label settings, while seamlessly integrating with other training strategies.

0 citationsRead paper

Learning to Perturb Hidden Representations for Generalizable Deep Learning

May 28, 2026

Existing deep learning approaches lack a unified and effective mechanism for perturbing hidden-layer activations, limiting their ability to systematically enhance model generalization. This work proposes a class-aware, learnable perturbation framework for activations (LPA), which, for the first time, systematically analyzes the underlying mechanisms of hidden activation perturbation and reveals the opposing effects of expansive versus contractive perturbations on generalization. By leveraging projected gradient descent (PGD) optimization, LPA enables class-adaptive perturbations and theoretically connects them to flat minima and inter-layer amplification effects. Extensive experiments demonstrate that LPA significantly outperforms current methods across balanced classification, long-tailed learning, and domain generalization tasks, and further exhibits complementary gains when combined with logit-level perturbation techniques such as LPL.

0 citationsRead paper