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 6601–6650 of 8378 papers

TitleStatusHype
Contrast and Classify: Training Robust VQA ModelsCode1
Shape-Texture Debiased Neural Network TrainingCode1
Monitoring War Destruction from Space: A Machine Learning Approach—0
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification—0
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy—0
EFSG: Evolutionary Fooling Sentences Generator—0
Pedestrian Trajectory Prediction with Convolutional Neural Networks—0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS—0
TransQuest at WMT2020: Sentence-Level Direct AssessmentCode1
PHICON: Improving Generalization of Clinical Text De-identification Models via Data AugmentationCode0
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network—0
Category-Learning with Context-Augmented Autoencoder—0
How Does Mixup Help With Robustness and Generalization?—0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification—0
Learning to Recombine and Resample Data for Compositional GeneralizationCode0
Affine-Invariant Robust Training—0
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language InferenceCode0
Population Based Training for Data Augmentation and Regularization in Speech Recognition—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
A disentangled generative model for disease decomposition in chest X-rays via normal image synthesisCode1
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANsCode1
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications—0
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation—0
Reward Machines: Exploiting Reward Function Structure in Reinforcement LearningCode1
On the Role of Supervision in Unsupervised Constituency Parsing—0
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering—0
Directional Graph NetworksCode1
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks—0
Image Translation for Medical Image Generation -- Ischemic Stroke LesionsCode0
SeqMix: Augmenting Active Sequence Labeling via Sequence MixupCode1
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning—0
A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet DatasetCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
GenAug: Data Augmentation for Finetuning Text GeneratorsCode1
Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses—0
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?—0
On the Effects of Knowledge-Augmented Data in Word Embeddings—0
Self-training Improves Pre-training for Natural Language UnderstandingCode1
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceCode0
Reverse Operation based Data Augmentation for Solving Math Word ProblemsCode0
Local Additivity Based Data Augmentation for Semi-supervised NERCode1
WeMix: How to Better Utilize Data Augmentation—0
Consensus Clustering With Unsupervised Representation Learning—0
3D-Aided Data Augmentation for Robust Face Understanding—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