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 4051–4100 of 8378 papers

TitleStatusHype
Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System—0
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization—0
Emotion Detection from EEG using Transfer Learning—0
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts—0
Enhancing Robustness of AI Offensive Code Generators via Data AugmentationCode0
KIT's Multilingual Speech Translation System for IWSLT 2023Code0
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems—0
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data AugmentationCode0
Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairsCode0
T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator—0
Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental HealthCode0
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao—0
An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language ModelsCode0
Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory—0
Learning to Substitute Spans towards Improving Compositional GeneralizationCode0
Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper—0
PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language ModelsCode0
R-Mixup: Riemannian Mixup for Biological Networks—0
Large Language Model Augmented Narrative Driven RecommendationsCode0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Generative Adversarial Networks for Data Augmentation—0
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning—0
Conditional Generation from Unconditional Diffusion Models using Denoiser RepresentationsCode0
Simple Data Augmentation Techniques for Chinese Disease NormalizationCode0
Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution DiscrepancyCode0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
Text Style Transfer Back-TranslationCode0
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting—0
SASMU: boost the performance of generalized recognition model using synthetic face dataset—0
Provable Benefit of Mixup for Finding Optimal Decision Boundaries—0
AfriNames: Most ASR models "butcher" African Names—0
CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification—0
A Novel Driver Distraction Behavior Detection Method Based on Self-supervised Learning with Masked Image ModelingCode0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations—0
On the Limitations of Temperature Scaling for Distributions with OverlapsCode0
Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks—0
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models—0
Fish-TViT: A novel fish species classification method in multi water areas based on transfer learning and vision transformer—0
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation—0
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures—0
MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden StatesCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing—0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—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