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 5051–5100 of 8378 papers

TitleStatusHype
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection—0
ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis—0
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks—0
Video Content Swapping Using GAN—0
Video Salient Object Detection via Fully Convolutional Networks—0
Video-to-Audio Generation with Hidden Alignment—0
Video Vision Transformers for Violence Detection—0
ViewCLR: Learning Self-supervised Video Representation for Unseen Viewpoints—0
View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis—0
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis—0
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
VirtualPainting: Addressing Sparsity with Virtual Points and Distance-Aware Data Augmentation for 3D Object Detection—0
Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture—0
Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD)—0
Vision-based Xylem Wetness Classification in Stem Water Potential Determination—0
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification—0
Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction—0
Vision transformers in domain adaptation and domain generalization: a study of robustness—0
VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction—0
Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings—0
Visual Data Augmentation through Learning—0
Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers—0
Visual Diver Face Recognition for Underwater Human-Robot Interaction—0
Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks—0
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network—0
Visual Speech Recognition in a Driver Assistance System—0
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning—0
Visual Transformers for Primates Classification and Covid Detection—0
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization—0
VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization—0
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes—0
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix—0
VNT-Net: Rotational Invariant Vector Neuron Transformers—0
VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING—0
Voice Conversion Can Improve ASR in Very Low-Resource Settings—0
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting—0
VoronoiPatches: Evaluating A New Data Augmentation Method—0
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion—0
WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks—0
Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network—0
Warping Resilient Scalable Anomaly Detection in Time Series—0
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models—0
Wasserstein Diffusion Tikhonov Regularization—0
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch—0
Water Bottle Defect Detection System Using Convolutional Neural Network—0
Wav2Vec-Aug: Improved self-supervised training with limited data—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Wavelet leader based formalism to compute multifractal features for classifying lung nodules in X-ray images—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