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 1801–1850 of 8378 papers

TitleStatusHype
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance DetectionCode0
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation—0
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-AdaptCode2
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion—0
LLM2LLM: Boosting LLMs with Novel Iterative Data EnhancementCode2
Vehicle Detection Performance in Nordic Region—0
IUST at ClimateActivism 2024: Towards Optimal Stance Detection: A Systematic Study of Architectural Choices and Data Cleaning TechniquesCode0
NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural NetworksCode0
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images—0
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception—0
What Matters for Active Texture Recognition With Vision-Based Tactile Sensors—0
RigorLLM: Resilient Guardrails for Large Language Models against Undesired ContentCode1
DreamDA: Generative Data Augmentation with Diffusion ModelsCode1
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
TexTile: A Differentiable Metric for Texture TileabilityCode1
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data SimulationCode0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
XPose: eXplainable Human Pose Estimation—0
Automated Contrastive Learning Strategy Search for Time Series—0
Do Generated Data Always Help Contrastive Learning?Code1
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Posterior Uncertainty Quantification in Neural Networks using Data AugmentationCode0
EffiPerception: an Efficient Framework for Various Perception Tasks—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
SETA: Semantic-Aware Token Augmentation for Domain GeneralizationCode1
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised LearningCode1
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
Investigating the Benefits of Projection Head for Representation Learning—0
MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image SegmentationCode0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Endora: Video Generation Models as Endoscopy Simulators—0
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationCode1
Scaling Data Diversity for Fine-Tuning Language Models in Human AlignmentCode1
CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation DataCode0
Multitask frame-level learning for few-shot sound event detection—0
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation frameworkCode2
A Versatile Framework for Multi-scene Person Re-identificationCode2
YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray ImagesCode1
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation—0
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization—0
Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive LearningCode0
Could We Generate Cytology Images from Histopathology Images? An Empirical Study—0
Efficient Diffusion-Driven Corruption Editor for Test-Time AdaptationCode0
SF(DA)^2: Source-free Domain Adaptation Through the Lens of Data AugmentationCode1
SOMson -- Sonification of Multidimensional Data in Kohonen Maps—0
A survey of synthetic data augmentation methods in computer vision—0
TRG-Net: An Interpretable and Controllable Rain GeneratorCode0
Revisiting Adversarial Training under Long-Tailed DistributionsCode2
Frozen Feature Augmentation for Few-Shot Image Classification—0
Show:102550
← PrevPage 37 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