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 5451–5500 of 8378 papers

TitleStatusHype
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks—0
Table-based Fact Verification with Salience-aware LearningCode0
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints—0
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence EmbeddingCode1
Learning with Different Amounts of Annotation: From Zero to Many LabelsCode0
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data—0
Smelting Gold and Silver for Improved Multilingual AMR-to-Text GenerationCode0
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story BooksCode1
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning ApproachCode0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
GOLD: Improving Out-of-Scope Detection in Dialogues using Data AugmentationCode1
Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays—0
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition—0
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis—0
Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation—0
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case—0
Data Augmentation for Cross-Domain Named Entity RecognitionCode1
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical ImagesCode1
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding—0
MitoDet: Simple and robust mitosis detection—0
Generative Models for Multi-Illumination Color Constancy—0
Transformer Networks for Data Augmentation of Human Physical Activity RecognitionCode1
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge—0
Application of Mix-Up Method in Document Classification Task Using BERT—0
Application of Deep Learning Methods to SNOMED CT Encoding of Clinical Texts: From Data Collection to Extreme Multi-Label Text-Based Classification—0
Solving SCAN Tasks with Data Augmentation and Input EmbeddingsCode0
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
DFKI SLT at GermEval 2021: Multilingual Pre-training and Data Augmentation for the Classification of Toxicity in Social Media CommentsCode0
Multi-Sample based Contrastive Loss for Top-k RecommendationCode1
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge—0
Spatio-temporal Self-Supervised Representation Learning for 3D Point CloudsCode1
Text AutoAugment: Learning Compositional Augmentation Policy for Text ClassificationCode1
Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech—0
Using convolutional neural networks for the classification of breast cancer imagesCode0
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERCode1
Cross-Lingual Text Classification of Transliterated Hindi and MalayalamCode0
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge—0
ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasetsCode1
Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization—0
AEDA: An Easier Data Augmentation Technique for Text ClassificationCode1
Open Set RF Fingerprinting using Generative Outlier Augmentation—0
InSE-NET: A Perceptually Coded Audio Quality Model based on CNN—0
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark DetectionCode1
High performing ensemble of convolutional neural networks for insect pest image detection—0
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
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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