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 6651–6700 of 8378 papers

TitleStatusHype
COVID-19 Classification of X-ray Images Using Deep Neural Networks—0
DecAug: Augmenting HOI Detection via Decomposition—0
Hard Negative Mixing for Contrastive LearningCode1
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning—0
Masked Face Recognition with Latent Part Detection—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Training Data Augmentation for Deep Learning Radio Frequency Systems—0
Understanding tables with intermediate pre-training—0
Deep learning for time series classificationCode2
Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and GenerationCode1
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation—0
Data augmentation as stochastic optimization—0
Data Instance Prior for Transfer Learning in GANs—0
Medical Image Segmentation Using Deep Learning: A SurveyCode0
Recognition and Synthesis of Object Transport Motion—0
iNLTK: Natural Language Toolkit for Indic LanguagesCode1
BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification ContextCode1
A little goes a long way: Improving toxic language classification despite data scarcityCode0
Empirical Study of Text Augmentation on Social Media Text in VietnameseCode0
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning—0
Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example SentencesCode1
Sub-graph Contrast for Scalable Self-Supervised Graph Representation LearningCode1
TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks—0
On Data Augmentation for Extreme Multi-label Classification—0
GraphCrop: Subgraph Cropping for Graph Classification—0
PodSumm -- Podcast Audio SummarizationCode2
Stereopagnosia: Fooling Stereo Networks with Adversarial PerturbationsCode1
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain RobustnessCode1
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the WildCode1
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired DataCode1
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image DerainingCode1
Encoding Robustness to Image Style via Adversarial Feature PerturbationsCode0
NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative—0
IDA: Improved Data Augmentation Applied to Salient Object DetectionCode1
Grounded Adaptation for Zero-shot Executable Semantic ParsingCode1
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk RepresentationCode1
SelfAugment: Automatic Augmentation Policies for Self-Supervised LearningCode1
DeepC2: AI-powered Covert Command and Control on OSNs—0
BOP Challenge 2020 on 6D Object LocalizationCode2
Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features—0
Decoupling Representation Learning from Reinforcement LearningCode2
Data Augmentation and Clustering for Vehicle Make/Model Classification—0
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field—0
Contrastive Self-supervised Learning for Graph Classification—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