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.

Further readings:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6601–6650 of 8378 papers

TitleStatusHype
Neural Photometry-guided Visual Attribute Transfer—0
Neural Proto-Language Reconstruction—0
Neural Radiance Field with LiDAR maps—0
Neural Rate Control for Video Encoding using Imitation Learning—0
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving—0
Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects—0
Neural representation of a time optimal, constant acceleration rendezvous—0
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation—0
Neural Style Transfer Enhanced Training Support For Human Activity Recognition—0
Neural Style Transfer for Synthesising a Dataset of Ancient Egyptian Hieroglyphs—0
Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension—0
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids—0
Neuro-Symbolic Ontology-Mediated Query Answering—0
New Results on Multi-Step Traffic Flow Prediction—0
NextBestPath: Efficient 3D Mapping of Unseen Environments—0
Next-Generation Phishing: How LLM Agents Empower Cyber Attackers—0
nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysis—0
NLPIITR at SemEval-2021 Task 6: RoBERTa Model with Data Augmentation for Persuasion Techniques Detection—0
NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset—0
No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasets—0
Noether: The More Things Change, the More Stay the Same—0
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign—0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
Noise Injection-based Regularization for Point Cloud Processing—0
Noising Scheme for Data Augmentation in Automatic Post-Editing—0
Noisy Adversarial Training—0
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening—0
NoisyMix: Boosting Model Robustness to Common Corruptions—0
Noisy student-teacher training for robust keyword spotting—0
Noisy Training Improves E2E ASR for the Edge—0
Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses—0
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework—0
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework—0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
Non-equilibrium molecular geometries in graph neural networks—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network—0
Non-Parallel Voice Conversion for ASR Augmentation—0
Nonparametric Bayes dynamic modeling of relational data—0
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Nonparametric posterior learning for emission tomography with multimodal data—0
Non-Parametric Priors For Generative Adversarial Networks—0
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition—0
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation—0
No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation—0
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition—0
NormAUG: Normalization-guided Augmentation for Domain Generalization—0
NOSE Augment: Fast and Effective Data Augmentation Without Searching—0
Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification—0
Show:102550
← PrevPage 133 of 168Next →
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