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Yanchen Xu

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Rectified Noise: A Generative Model Using Positive-incentive Noise

Zhenyu Gu
Yanchen Xu
Sida Huang
Yubin Guo
Hongyuan Zhang

Rectified Flow (RF) has been widely used as an effective generative model. Although RF is primarily based on probability flow Ordinary Differential Equations (ODE), recent studies have shown that injecting noise through reverse-time Stochastic Differential Equations (SDE) for sampling can achieve superior generative performance. Inspired by Positive-incentive Noise (Pi-noise), we propose an innovative generative algorithm to train Pi-noise generators, namely Rectified Noise (RN), which improves the generative performance by injecting Pi-noise into the velocity field of pre-trained RF models. After introducing the Rectified Noise pipeline, pre-trained RF models can be efficiently transformed into Pi-noise generators. We validate Rectified Noise by conducting extensive experiments across various model architectures on different datasets. Notably, we find that: (1) RF models using Rectified Noise reduce FID from10.16 to 9.05 on ImageNet-1k. (2) The models of Pi-noise generators achieve improved performance with only 0.39% additional training parameters.

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ICML Conference 2025 Conference Paper

Learn Beneficial Noise as Graph Augmentation

Siqi Huang
Yanchen Xu
Hongyuan Zhang 0001
Xuelong Li 0001

Although graph contrastive learning (GCL) has been widely investigated, it is still a challenge to generate effective and stable graph augmentations. Existing methods often apply heuristic augmentation like random edge dropping, which may disrupt important graph structures and result in unstable GCL performance. In this paper, we propose P ositive- i ncentive N oise driven G raph D ata A ugmentation (PiNGDA), where positive-incentive noise (pi-noise) scientifically analyzes the beneficial effect of noise under the information theory. To bridge the standard GCL and pi-noise framework, we design a Gaussian auxiliary variable to convert the loss function to information entropy. We prove that the standard GCL with pre-defined augmentations is equivalent to estimate the beneficial noise via the point estimation. Following our analysis, PiNGDA is derived from learning the beneficial noise on both topology and attributes through a trainable noise generator for graph augmentations, instead of the simple estimation. Since the generator learns how to produce beneficial perturbations on graph topology and node attributes, PiNGDA is more reliable compared with the existing methods. Extensive experimental results validate the effectiveness and stability of PiNGDA.

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JBHI Journal 2025 Journal Article

RPD: Regional Prior Distillation for Breast Cancer Diagnosis in Ultrasound Images

Yi Lin
Haosen Wang
Yingnan Zhao
Dan Lu
Yanchen Xu
Jiexiao Xue
Xi Chen
Jingchi Jiang

Breast cancer is the leading cause of death among women worldwide. Ultrasound imaging is an important means for the early detection of breast cancer, improving the survival rate. Due to the shortage of experienced sonographers, computer-aided systems for breast cancer recognition become particularly important. Some recent studies analyze tumor types in lesion regions but rely on predefined ROIs. Some other studies recognize cancer in the whole ultrasound image, but always suffer from the extremely variable proportion, location and quantity of the tumor lesions. In this paper, we propose a regional prior distillation (RPD) framework for breast cancer diagnosis in ultrasound images. To enhance the analysis of the tumor region, we propose an Image-Cross Attention (ICA) to fuse the predefined ROI prior information with ultrasound images and train a prior-fused model. To remove the constraint of predefined ROIs, we propose a Distribution Distillation Learning (DDL) to distill the prior-fused sample distribution from the prior-fused model into a diagnostic model, which analyzes the disease from only ultrasound images, based on the knowledge distillation paradigm of the teacher-student framework. Comprehensive experiments are conducted on multi-institutional datasets to validate the proposed RPD framework. The results demonstrate the following points. The ICA fuses regional prior information adequately, leading to a high-performance prior-fused model. The DDL distills the prior information effectively, enhancing the diagnostic model to focus on the tumor lesions. The performance of the diagnostic model surpasses that of current SOTA methods by 1. 66% in accuracy and 0. 64% in AUC. In addition, the diagnostic model is robust to slight perturbations and achieves good generalization performance.

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AAAI Conference 2025 Conference Paper

Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?

Yanchen Xu
Siqi Huang
Hongyuan Zhang
Xuelong Li

Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable graph augmentation to generating proper views for contrastive learning remains an essential problem. Dropping edges is a primary augmentation in GCL while adding edges is not a common method due to its unstable performance. To our best knowledge, there is no theoretical analysis to study why dropping edges usually outperforms adding edges. To answer this question, we introduce a new metric, namely Error Passing Rate (EPR), to quantify how a graph fits the network. Inspired by the theoretical conclusions and the idea of positive-incentive noise, we propose a novel GCL algorithm, Error-PAssing-based Graph Contrastive Learning (EPAGCL), which uses both edge adding and edge dropping as its augmentations. To be specific, we generate views by adding and dropping edges based on the weights derived from EPR. Extensive experiments on various real-world datasets are conducted to validate the correctness of our theoretical analysis and the effectiveness of our proposed algorithm.

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