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 3101–3150 of 8378 papers

TitleStatusHype
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge—0
Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data GenerationCode0
The Training Process of Many Deep Networks Explores the Same Low-Dimensional ManifoldCode1
MASNet: A Robust Deep Marine Animal Segmentation NetworkCode0
Part Aware Contrastive Learning for Self-Supervised Action RecognitionCode1
A Simplified Framework for Contrastive Learning for Node Representations—0
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation—0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
Optimizing the AI Development Process by Providing the Best Support Environment—0
Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal DataCode0
Semi-Supervised RF Fingerprinting with Consistency-Based Regularization—0
Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
Generating images of rare concepts using pre-trained diffusion modelsCode1
Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditionsCode0
Adversarial Policy Optimization in Deep Reinforcement Learning—0
HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score FilteringCode0
NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques—0
Self-discipline on multiple channelsCode0
ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT—0
The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification TasksCode1
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
Exploiting CNNs for Semantic Segmentation with Pascal VOC—0
Implicit Counterfactual Data Augmentation for Robust Learning—0
Learning to Predict Navigational Patterns from Partial ObservationsCode1
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation—0
AutoCure: Automated Tabular Data Curation Technique for ML PipelinesCode0
Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient TuningCode0
NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset—0
Segmentation of Hemorrhagic Areas in Human Brain from CT Scan ImagesCode0
Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning—0
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation—0
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection—0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection—0
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation—0
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification—0
CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information RetrievalCode0
OpenMix+: Revisiting Data Augmentation for Open Set RecognitionCode0
Image retrieval outperforms diffusion models on data augmentation—0
Motion Artifacts Detection in Short-scan Dental CBCT Reconstructions—0
OLISIA: a Cascade System for Spoken Dialogue State TrackingCode0
Enhancing object detection robustness: A synthetic and natural perturbation approach—0
Is augmentation effective to improve prediction in imbalanced text datasets?—0
LA3: Efficient Label-Aware AutoAugmentCode0
Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras—0
Denoising Diffusion Medical Models—0
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning—0
Tailoring Domain Adaptation for Machine Translation Quality EstimationCode0
Robustness of Visual Explanations to Common Data AugmentationCode0
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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