Mixup

Follow this topic Ask about this topic

Momentum

0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 12

All topics

DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Features
Aug 5, 2026Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting ChenDisentangled Representation Learning Data Augmentation
1AIML, Adelaide University, Australia · 2CVSSP, University of Surrey, UK
Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss
Jun 29, 2026Nian Shao, Xian Li, Xiaofei LiContrastive Learning Sound Event Detection
1Zhejiang University, Hangzhou, China. · School of Engineering, Westlake University, Hangzhou, China · 3Westlake University & Westlake Institute for Advanced Study, Hangzhou, China.
$S^{2}$ -FracMix: Label-Preserving Self-Saliency Mixup Augmentation
Jun 24, 2026Khawar Islam, Arif Mahmood, Xin Jin +1Data Augmentation Mixup
The University of Melbourne, Australia · Information Technology University, Pakistan · Westlake University, China
Enhancing Protein Representation Learning via Manifold Restore Mixing
Jun 21, 2026Yizhou Dang, Chuang Zhao, Lianbo Ma +3Data Augmentation Mixup
Software College, Northeastern University Shenyang, China · Tianjin University Tianjin, China · School of Computer Science and Engineering, Northeastern University Shenyang, China +1
Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions
Jun 10, 2026José Medina, Paul Honeine, Abdelaziz Bensrhair +1Data Augmentation Model Calibration
ITS Lab, Institute of Computer Science, University of Tartu, Estonia. · LITIS, Universit´e de Rouen, France. · LITIS, INSA de Rouen, France.
SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
Jun 7, 2026Hongyi Yu, Yaoyu Fang, Jiahe Qian +3Synthetic Data Augmentation Spatial Transcriptomics
Northwestern University, Department of Radiology, Chicago, IL, USA · Yale University, Department of Biostatistics, New Haven, CT, USA · Northwestern University, Department of Cell and Developmental Biology, Chicago, IL, USA +1
Image Feature Fusion-based Federated Client Unlearning (FCU)
May 26, 2026Hangyi Shen, Yizhi Pan, Tiansuo Li +2Federated Unlearning Mixup
11Hangzhou Medical College, Hangzhou, China
Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label
May 20, 2026Jingyang Mao, Ningkang Peng, Yanhui GuClassification Learning with Noisy Labels
Nanjing Normal University Nanjing, China
Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network
May 16, 2026Baogui Huan, Chuanzheng Gong, Dezhong Chen +3Dynamic Convolution Data Augmentation
State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266100, China · Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762 USA
HiMix: Hierarchical Artifact-aware Mixup for Generalized Synthetic Image Detection
Apr 30, 2026Shuchang Zhou, Kaiwen Shen, Jiwei Wei +3Synthetic Data Augmentation Image Forgery Detection
University of Electronic Science and Technology of China · Hainan University
AnemiaVision: Non-Invasive Anemia Detection via Smartphone Imagery Using EfficientNet-B3 with TrivialAugmentWide, Mixup Augmentation, and Persistent Patient History Management
Apr 24, 2026Rahul PatelMedical Imaging Medical Image Classification
Department of Electronics and Communication Engineering Indian Institute of Information Technology, Surat India
Improving Mixup Calibration with Wasserstein Distributionally Robust Optimization
Jun 22, 2025Jiaming Hu, Yeping Jin, Debarghya Mukherjee +1Image Corruption Robustness Data Augmentation
Department of Mathematics & Statistics Boston University · Department of Electrical and Computer Engineering, Division of Systems Engineering Department of Biomedical Engineering Faculty of Computing & Data Sciences Boston University

Aug 5, 2026, cs.CVJ/K move · Enter open · S save

DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Features

Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting Chen

1AIML, Adelaide University, Australia · 2CVSSP, University of Surrey, UK

Disentangled Representation Learning Data Augmentation Medical Image Classification Ordinal Regression Mixup

Open paper AskPDF arXiv

Image mixup is a widely adopted data augmentation strategy, yet it is ill-suited for ordinal classification tasks such as medical disease grading, where labels encode a progression of severity. By indiscriminately blending disease-severity cues (ordinal) with appearance-level variation (non-ordinal), standard mixup produces samples that distort the very ordinal structure that underpins clinical severity grading. We introduce DisMix, an order-aware mixup framework for ordinal classification. DisMix disentangles ordinal and non-ordinal features via a dual-codebook VQ-VAE, allowing each subspace to be mixed independently: ordinal codes are interpolated to produce meaningful intermediate ranks, while non-ordinal codes are varied to introduce appearance diversity without corrupting the ordinal signal. Across four medical imaging datasets, DisMix shows the best aggregate performance among six image mixup baselines paired with six ordinal classifiers and remains effective under data scarcity and clinical grading variability.

Momentum

Related topics

Latest papers 12