📄 A survey on Image Data Augmentation for Deep Learning 📄 Adding noise 📄 Adversarial Spatial Dropout for Occlusion 📄 Alleviating Class Imbalance with Data Augmentation 📄 Attentive CutMix 📄 AttributeMix 📄 Augmentation-wise Weight Sharing strategy 📄 Augmented Random Search 📄 AugMix 📄 Auto Augment 📄 AutoAugment 📄 Co-Mixup 📄 Color Space Transformations 📄 CowMask 📄 Cropping 📄 Cut and Delete 📄 Cut and Mix 📄 Cut, Paste and Learn 📄 CutMix 📄 Cutout 📄 Data aug for spoken language 📄 Data Augmentation with Curriculum Learning 📄 Deep Generative Models 📄 Fast AutoAugment 📄 FeatMatch 📄 Feature Augmentation 📄 Feature Space Augmentation 📄 Flipping 📄 Fmix 📄 GAN‐based Data Augmentation 📄 Gaussian Distortion 📄 Geometric Transformations 📄 GridMask 📄 Hide and Seek 📄 Image Erasing 📄 Image Manipulation 📄 Image Mix 📄 Image Mixing and Deletion 📄 Intra-Class Part Swapping 📄 KeepAugment 📄 Kernel Filters 📄 Manifold MixUp 📄 ManifoldMix 📄 Meta Learning Data Augmentations 📄 Mixed Example 📄 Moment Exchange 📄 Neural Augmentation 📄 Noise Injection 📄 On the Importance of Visual Context for Data Augmentation in Scene Understanding 📄 Population Based Augmentation 📄 Puzzle Mix 📄 Random Distortion 📄 Random Erasing 📄 ReMix 📄 ResizeMix 📄 RICAP 📄 SaliencyMix 📄 Sample Pairing 📄 Shear 📄 Skew Tilt 📄 Smart Augmentation 📄 SmoothMix 📄 SMOTE 📄 SnapMix 📄 SpecAugment 📄 Test-time Augmentation 📄 Visual Context Augmentation