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 6751–6800 of 8378 papers

TitleStatusHype
On the Performance of Convolutional Neural Networks under High and Low Frequency Information—0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
On the Reproducibility of Neural Network Predictions—0
On the Robustness of Human-Object Interaction Detection against Distribution Shift—0
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks—0
On the Role of Supervision in Unsupervised Constituency Parsing—0
On the (Un-)Avoidability of Adversarial Examples—0
On the Usability of Transformers-based models for a French Question-Answering task—0
On the Usefulness of Synthetic Tabular Data Generation—0
On the Way to LLM Personalization: Learning to Remember User Conversations—0
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020—0
On Training Sketch Recognizers for New Domains—0
On Using SpecAugment for End-to-End Speech Translation—0
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Open data for Moroccan license plates for OCR applications : data collection, labeling, and model construction—0
Open Set RF Fingerprinting using Generative Outlier Augmentation—0
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti—0
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery—0
OptGAN: Optimizing and Interpreting the Latent Space of the Conditional Text-to-Image GANs—0
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions—0
Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study—0
Optimal Layer Selection for Latent Data Augmentation—0
Optimal Resource Allocation for Serverless Queries—0
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation—0
Optimal Transport-Based Displacement Interpolation with Data Augmentation for Reduced Order Modeling of Nonlinear Dynamical Systems—0
Integrating Semantic Knowledge to Tackle Zero-shot Text ClassificationCode0
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAECode0
Training Structured Neural Networks Through Manifold Identification and Variance ReductionCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-PastingCode0
Practical Deep Learning with Bayesian PrinciplesCode0
Analytical Moment Regularizer for Gaussian Robust NetworksCode0
Practical Transformer-based Multilingual Text ClassificationCode0
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance SegmentationCode0
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem SolversCode0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix QualityCode0
Leveraging QA Datasets to Improve Generative Data AugmentationCode0
Insect Identification in the Wild: The AMI DatasetCode0
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification TrackCode0
Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot ClassificationCode0
Predicting Confusion from Eye-Tracking Data with Recurrent Neural NetworksCode0
Artificial Intelligence for Biomedical Video GenerationCode0
Simplicial RegularizationCode0
Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy DataCode0
Exploring the Landscape of Spatial RobustnessCode0
Simplifying Neural Network Training Under Class ImbalanceCode0
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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