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 6601–6650 of 8378 papers

TitleStatusHype
Data-Driven Incident Detection in Power Distribution Systems—0
Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective—0
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data—0
3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges—0
Data-Efficient Methods for Dialogue Systems—0
Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection—0
Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection—0
Data for free: Fewer-shot algorithm learning with parametricity data augmentation—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Data Instance Prior for Transfer Learning in GANs—0
Data InStance Prior (DISP) in Generative Adversarial Networks—0
Data Interpolating Prediction: Alternative Interpretation of Mixup—0
DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization—0
Data Priming Network for Automatic Check-Out—0
Data Scarcity in Recommendation Systems: A Survey—0
Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora—0
Data Techniques For Online End-to-end Speech Recognition—0
DATScore: Evaluating Translation with Data Augmented Translations—0
D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes—0
DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets—0
DAWSON: Data Augmentation using Weak Supervision On Natural Language—0
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition—0
DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability—0
D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation—0
DD-TIG at Constraint@ACL2022: Multimodal Understanding and Reasoning for Role Labeling of Entities in Hateful Memes—0
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil—0
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations—0
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning—0
De-Bias for Generative Extraction in Unified NER Task—0
Debiasing Multimodal Sarcasm Detection with Contrastive Learning—0
DecAug: Augmenting HOI Detection via Decomposition—0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks—0
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation—0
Decoding EEG Speech Perception with Transformers and VAE-based Data Augmentation—0
Decoding Part-of-Speech from Human EEG Signals—0
Decoding Taste Information in Human Brain: A Temporal and Spatial Reconstruction Data Augmentation Method Coupled with Taste EEG—0
Decomposed Mutual Information Estimation for Contrastive Representation Learning—0
Decoupled Diffusion Sparks Adaptive Scene Generation—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach—0
Deep CNN Ensemble with Data Augmentation for Object Detection—0
Deep CNNs for Peripheral Blood Cell Classification—0
Deep Convolutional Neural Network-Based Autonomous Drone Navigation—0
Deep Convolutional Neural Network for Roadway Incident Surveillance Using Audio Data—0
Deep convolutional recurrent neural network for short-interval EEG motor imagery classification—0
Deep COVID-19 Forecasting for Multiple States with Data Augmentation—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