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 4251–4300 of 8378 papers

TitleStatusHype
SGA: A Graph Augmentation Method for Signed Graph Neural Networks—0
SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism—0
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming—0
ShapeAug++: More Realistic Shape Augmentation for Event Data—0
ShapeAug: Occlusion Augmentation for Event Camera Data—0
ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation—0
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images—0
Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach—0
SHARE: Single-view Human Adversarial REconstruction—0
Sharing Data by Language Family: Data Augmentation for Romance Language Morpheme Segmentation—0
Sufficient Invariant Learning for Distribution Shift—0
Sharpness & Shift-Aware Self-Supervised Learning—0
Sheffield Submissions for WMT18 Multimodal Translation Shared Task—0
SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator Asynchrony Detection—0
Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning—0
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention—0
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network—0
Siamese Masked Autoencoders—0
SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models—0
SI-FID: Only One Objective Indicator for Evaluating Stitched Images—0
Signed Input Regularization—0
Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition—0
Sim2Real Transfer for Audio-Visual Navigation with Frequency-Adaptive Acoustic Field Prediction—0
SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks—0
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research—0
SimCURL: Simple Contrastive User Representation Learning from Command Sequences—0
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation—0
SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders—0
SimGen: Simulator-conditioned Driving Scene Generation—0
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search—0
Similarity-Guided Diffusion for Contrastive Sequential Recommendation—0
Similar Scenes arouse Similar Emotions: Parallel Data Augmentation for Stylized Image Captioning—0
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images—0
Simple and Effective Augmentation Methods for CSI Based Indoor Localization—0
Simple and effective data augmentation for compositional generalization—0
Simple Contrastive Graph Clustering—0
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging—0
Simple In-place Data Augmentation for Surveillance Object Detection—0
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification—0
Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers—0
Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data—0
Simulated LiDAR Repositioning: a novel point cloud data augmentation method—0
Simulating ASR errors for training SLU systems—0
Simulating dysarthric speech for training data augmentation in clinical speech applications—0
Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership—0
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic 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 2×112d) (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