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 6051–6100 of 8378 papers

TitleStatusHype
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
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding—0
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge—0
MitoDet: Simple and robust mitosis detection—0
Generative Models for Multi-Illumination Color Constancy—0
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
Application of Deep Learning Methods to SNOMED CT Encoding of Clinical Texts: From Data Collection to Extreme Multi-Label Text-Based Classification—0
DFKI SLT at GermEval 2021: Multilingual Pre-training and Data Augmentation for the Classification of Toxicity in Social Media CommentsCode0
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge—0
Application of Mix-Up Method in Document Classification Task Using BERT—0
Solving SCAN Tasks with Data Augmentation and Input EmbeddingsCode0
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge—0
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
Cross-Lingual Text Classification of Transliterated Hindi and MalayalamCode0
InSE-NET: A Perceptually Coded Audio Quality Model based on CNN—0
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations—0
Open Set RF Fingerprinting using Generative Outlier Augmentation—0
Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
High performing ensemble of convolutional neural networks for insect pest image detection—0
ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic SegmentationCode0
Data Augmentation for Low-Resource Named Entity Recognition Using BacktranslationCode0
Similar Scenes arouse Similar Emotions: Parallel Data Augmentation for Stylized Image Captioning—0
StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Influence-guided Data Augmentation for Neural Tensor CompletionCode0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word EmbeddingsCode0
DTWSSE: Data Augmentation with a Siamese Encoder for Time Series—0
A Unified Transformer-based Framework for Duplex Text Normalization—0
Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender SystemsCode0
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling—0
Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction—0
Neural TMDlayer: Modeling Instantaneous flow of features via SDE GeneratorsCode0
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image CaptioningCode0
Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data—0
Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained ModelsCode0
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space—0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Directing the violence or admonishing it? A survey of contronymy and androcentrism in Google Translate and some recommendationsCode0
An Empirical Survey of Data Augmentation \ Limited Data Learning in NLP—0
Adapting Multilingual Models for Code-Mixed Translation using Back-to-Back Translation—0
A Comparison of Strategies for Source-Free Domain Adaptation—0
Tailor: Generating and Perturbing Text with Semantic Controls—0
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification TasksCode0
Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset—0
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance—0
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified