SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

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Papers

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Showing 3701–3750 of 8378 papers

TitleStatusHype
Gibbs Max-margin Topic Models with Data Augmentation—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle—0
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention—0
Global Context Is All You Need for Parallel Efficient Tractography Parcellation—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Creation of Novel Soft Robot Designs using Generative AI—0
Global Mixup: Eliminating Ambiguity with Clustering Relationships—0
Global Mixup: Eliminating Ambiguity with Clustering—0
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning—0
Global Wheat Challenge 2020: Analysis of the competition design and winning models—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022—0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering—0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation—0
Boosting Event Extraction with Denoised Structure-to-Text Augmentation—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Good Data Is All Imitation Learning Needs—0
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models—0
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition—0
Good-Enough Example Extrapolation—0
Gophormer: Ego-Graph Transformer for Node Classification—0
Go Small and Similar: A Simple Output Decay Brings Better Performance—0
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces—0
Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
Diffusion Model with Clustering-based Conditioning for Food Image Generation—0
A Competitive Method to VIPriors Object Detection Challenge—0
Gradient-based Data Augmentation for Semi-Supervised Learning—0
Gradient Mask: Lateral Inhibition Mechanism Improves Performance in Artificial Neural Networks—0
GradMix for nuclei segmentation and classification in imbalanced pathology image datasets—0
Diffusion Models for Robotic Manipulation: A Survey—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning—0
Graph Augmentation for Cross Graph Domain Generalization—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation—0
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection—0
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts—0
Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation—0
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization—0
GraphCrop: Subgraph Cropping for Graph Classification—0
Diffusion Bridge Models for 3D Medical Image Translation—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation—0
iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified