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 7751–7800 of 8378 papers

TitleStatusHype
Improved Regularization Techniques for End-to-End Speech Recognition—0
Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation—0
Improved resistance of neural networks to adversarial images through generative pre-training—0
Improved Selective Refinement Network for Face Detection—0
Improved singing voice separation with chromagram-based pitch-aware remixing—0
Improved Techniques for the Conditional Generative Augmentation of Clinical Audio Data—0
Improved Techniques For Weakly-Supervised Object Localization—0
Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks—0
Improve Learning from Crowds via Generative Augmentation—0
Improving 3D Object Detection through Progressive Population Based Augmentation—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—0
Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models—0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Exploring Train and Test-Time Augmentations for Audio-Language Learning—0
Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation—0
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks—0
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation—0
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation—0
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals—0
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation—0
Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation—0
Improving Conditioning in Context-Aware Sequence to Sequence Models—0
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation—0
Improving COVID-19 CXR Detection with Synthetic Data Augmentation—0
Improving Crowded Object Detection via Copy-Paste—0
Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation—0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
Improving Data Driven Inverse Text Normalization using Data Augmentation—0
Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models—0
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey—0
Improving Deep Learning using Generic Data Augmentation—0
Improving Depression estimation from facial videos with face alignment, training optimization and scheduling—0
Improving Dialogue Breakdown Detection with Semi-Supervised Learning—0
Improving Discriminative Visual Representation Learning via Automatic Mixup—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models—0
Improving End-to-End Models for Set Prediction in Spoken Language Understanding—0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning—0
Improving extreme weather events detection with light-weight neural networks—0
Improving Face Detection Performance with 3D-Rendered Synthetic Data—0
Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets—0
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning—0
Improving GANs with A Dynamic Discriminator—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