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 5851–5900 of 8378 papers

TitleStatusHype
UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation—0
DEFT 2021: Évaluation automatique de réponses courtes, une approche basée sur la sélection de traits lexicaux et augmentation de données (DEFT 2021 : Automatic short answer grading, a lexical features selection and data augmentation based approach)Code0
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question AnsweringCode0
Training Data Augmentation for Code-Mixed TranslationCode0
Training Language Models under Resource Constraints for Adversarial Advertisement Detection—0
Practical Transformer-based Multilingual Text ClassificationCode0
How low is too low? A monolingual take on lemmatisation in Indian languages—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Generalization in Instruction Following Systems—0
Counterfactual Data Augmentation for Neural Machine TranslationCode1
Target-Aware Data Augmentation for Stance Detection—0
Raman spectral analysis of mixtures with one-dimensional convolutional neural network—0
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021—0
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction—0
Mixup for Node and Graph ClassificationCode1
Concurrent Adversarial Learning for Large-Batch Training—0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models—0
An ordinal CNN approach for the assessment of neurological damage in Parkinson's disease patientsCode0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizationCode0
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features—0
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift—0
Cascaded Diffusion Models for High Fidelity Image Generation—0
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness—0
Towards More Equitable Question Answering Systems: How Much More Data Do You Need?Code0
Data Augmentation for Text Generation Without Any Augmented Data—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMaxCode0
Contrastive Fine-tuning Improves Robustness for Neural Rankers—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition—0
Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection—0
Enhance Multimodal Model Performance with Data Augmentation: Facebook Hateful Meme Challenge SolutionCode0
GraphVICRegHSIC: Towards improved self-supervised representation learning for graphs with a hyrbid loss functionCode0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
A Fourier-based Framework for Domain GeneralizationCode1
Grounding inductive biases in natural images: invariance stems from variations in dataCode1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active LearningCode1
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net—0
Properties of the After Kernel—0
Combining Transformer Generators with Convolutional Discriminators—0
SmartPatch: Improving Handwritten Word Imitation with Patch DiscriminatorsCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
Contrastive Learning for Many-to-many Multilingual Neural Machine TranslationCode1
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray imagesCode0
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
Exploring The Limits Of Data Augmentation For Retinal Vessel SegmentationCode1
A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep LearningCode0
Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine—0
Vision Transformer for Fast and Efficient Scene Text RecognitionCode1
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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