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 6101–6150 of 8378 papers

TitleStatusHype
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation—0
Cascade Bagging for Accuracy Prediction with Few Training SamplesCode0
Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral LossCode0
An empirical investigation into audio pipeline approaches for classifying bird species—0
InfoGAN-MSF: a data augmentation approach for correlative bridge monitoring factors—0
Towards artificially intelligent recycling Improving image processing for waste classification—0
Triplet Contrastive Learning for Brain Tumor Classification—0
Impact of Aliasing on Generalization in Deep Convolutional Networks—0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
High-frequency shape recovery from shading by CNN and domain adaptation—0
Distilling Transformers for Neural Cross-Domain Search—0
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning—0
Automatic Rail Component Detection Based on AttnConv-Net—0
Exploring Structure Consistency for Deep Model Watermarking—0
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
Alleviating Mode Collapse in GAN via Diversity Penalty Module—0
Pervasive Hand Gesture Recognition for Smartphones using Non-audible Sound and Deep Learning—0
MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease—0
Lung Sound Classification Using Co-tuning and Stochastic Normalization—0
Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney—0
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose—0
Adversarial Data Augmentation for Disordered Speech Recognition—0
Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR—0
Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter DatasetCode0
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation—0
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER—0
LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context Disambiguation—0
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification—0
ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task—0
Data augmentation for low-resource grapheme-to-phoneme mapping—0
PAW at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation : Exploring Cross Lingual Transfer, Augmentations and Adversarial Training—0
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 2021—0
基于字词粒度噪声数据增强的中文语法纠错(Chinese Grammatical Error Correction enhanced by Data Augmentation from Word and Character Levels)—0
Building Goal-oriented Document-grounded Dialogue Systems—0
Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach—0
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes—0
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation—0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation—0
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes—0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizabilityCode0
Discriminative Reranking for Neural Machine Translation—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising—0
Technical Report on Shared Task in DialDoc21—0
BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
NLPIITR at SemEval-2021 Task 6: RoBERTa Model with Data Augmentation for Persuasion Techniques Detection—0
LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data Augmentation—0
Improved English to Hindi Multimodal Neural Machine Translation—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