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.

Further readings:

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Papers

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Showing 6001–6050 of 8378 papers

TitleStatusHype
Track, Check, Repeat: An EM Approach to Unsupervised Tracking—0
Correlation Sketches for Approximate Join-Correlation Queries—0
Regularizing Generative Adversarial Networks under Limited DataCode1
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Incremental Generative Occlusion Adversarial Suppression Network for Person ReIDCode1
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Generalization of GANs and overparameterized models under Lipschitz continuity—0
Weakly supervised segmentation with cross-modality equivariant constraintsCode1
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure SystemsCode1
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems—0
Topological Regularization for Graph Neural Networks Augmentation—0
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRCode0
MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks—0
Neural Network Robustness as a Verification Property: A Principled Case StudyCode0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty CalibrationCode0
Data Augmentation with Manifold Barycenters—0
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural NetworkCode0
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation—0
A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests—0
GABO: Graph Augmentations with Bi-level Optimization—0
EfficientNetV2: Smaller Models and Faster TrainingCode3
Few-shot learning through contextual data augmentationCode0
SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification—0
Scale-aware Automatic Augmentation for Object DetectionCode1
ReMix: Towards Image-to-Image Translation with Limited DataCode1
Rainbow Memory: Continual Learning with a Memory of Diverse SamplesCode1
An Exploration of Data Augmentation Techniques for Improving English to Tigrinya Translation—0
Data augmentation for dealing with low sampling rates in NILM—0
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction—0
Improving robustness against common corruptions with frequency biased models—0
Unsupervised Disentanglement of Linear-Encoded Facial Semantics—0
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial TrainingCode0
Learning Representational Invariances for Data-Efficient Action RecognitionCode1
Learning Domain Invariant Representations for Generalizable Person Re-Identification—0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
Improved Meta-Learning Training for Speaker Verification—0
Contextual Scene Augmentation and Synthesis via GSACNet—0
AlignMixup: Improving Representations By Interpolating Aligned FeaturesCode1
Noise Injection-based Regularization for Point Cloud Processing—0
Representation Learning by Ranking under multiple tasks—0
Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data—0
Self-supervised Graph Neural Networks without explicit negative samplingCode1
Unsupervised Document Embedding via Contrastive Augmentation—0
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity MaximizationCode1
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers—0
An Approach to Improve Robustness of NLP Systems against ASR Errors—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