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 5301–5350 of 8378 papers

TitleStatusHype
Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection—0
Synthetic Data from Diffusion Models Improves ImageNet Classification—0
Synthetic Data Generation for Augmenting Small Samples—0
Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure—0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks—0
Synthetic Latent Fingerprint Generator—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Synthetic Sample Selection via Reinforcement Learning—0
Synthetic speech detection using meta-learning with prototypical loss—0
Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study—0
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency—0
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic—0
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task—0
SZU-AFS Antispoofing System for the ASVspoof 5 Challenge—0
T\"ubingen-Oslo system at SIGMORPHON shared task on morphological inflection. A multi-tasking multilingual sequence to sequence model.—0
Table-based Fact Verification with Self-labeled Keypoint Alignment—0
Tackling Diverse Minorities in Imbalanced Classification—0
Tackling Occlusion in Siamese Tracking with Structured Dropouts—0
T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator—0
TADA: Temporal Adversarial Data Augmentation for Time Series Data—0
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection—0
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation—0
Tailor: Generating and Perturbing Text with Semantic Controls—0
Target-Aware Contextual Political Bias Detection in News—0
Target-Aware Data Augmentation for Stance Detection—0
Target-centered Subject Transfer Framework for EEG Data Augmentation—0
Targeted Augmentation for Low-Resource Event Extraction—0
Targeted Data Augmentation for bias mitigation—0
Targeted Data Generation: Finding and Fixing Model Weaknesses—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness—0
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation—0
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10—0
Task Oriented In-Domain Data Augmentation—0
Task Progressive Curriculum Learning for Robust Visual Question Answering—0
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge—0
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging—0
TBGC: Task-level Backbone-Oriented Gradient Clip for Multi-Task Foundation Model Learning—0
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation—0
TBNet:Pulmonary Tuberculosis Diagnosing System using Deep Neural Networks—0
TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction—0
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise—0
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation—0
Teach me how to Interpolate a Myriad of Embeddings—0
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing—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