awesome-mix
github.com/chengtaicao/awesome-mix ↗A curated list of awesome Mix
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MethodologyMixDA ApplicationsExplainability Analysis of MixDARelated Project
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me adversarial training resources from awesome-mix"
Installation instructions →What's inside
MixDA Applications
- Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial TrainingAdversarial Training
- AdvAug: Robust Adversarial Augmentation for Neural Machine TranslationNatural Language Processing
- Adversarial Domain Adaptation with Domain MixupDomain Adaption
- Adversarially Optimized Mixup for Robust ClassificationAdversarial Training
- Adversarial Vertex Mixup: Toward Better Adversarially Robust GeneralizationAdversarial Training
- A Simple Data Mixing Prior for Improving Self-Supervised LearningContrastive Learning
Methodology
- Adversarial Mixing Policy for Relaxing Locally Linear Constraints in MixupMixup-based
- AlignMixup: Improving Representations by Interpolating Aligned FeaturesMixup-based
- Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning based Image ClassificationCutmix-based
- Attribute Mix: Semantic Data Augmentation for Fine Grained RecognitionCutmix-based
- AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyBeyond Mixup & Cutmix
- AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization PerformanceBeyond Mixup & Cutmix
Explainability Analysis of MixDA
- A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function PerspectiveModel Regularization
- How does Mixup Help with Robustness and Generalization?Model Regularization
- On Mixup RegularizationModel Regularization
- On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural NetworksUncertainty & Calibration
- Understanding Mixup Training MethodsModel Regularization
- Vicinal Risk MinimizationVicinal Risk Minimization
Related Project
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