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:

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2751–2800 of 8378 papers

TitleStatusHype
Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detection—0
DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding—0
Domain-guided data augmentation for deep learning on medical imaging—0
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems—0
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification—0
Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer—0
Anticipating the Unseen Discrepancy for Vision and Language Navigation—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
Evaluating the Impact of Data Augmentation on Predictive Model Performance—0
Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection—0
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction—0
Domain Generalization via Balancing Training Difficulty and Model Capability—0
Dynamic Feature Learning and Matching for Class-Incremental Learning—0
Dynamic Gesture Recognition—0
Dynamic hysteresis model of grain-oriented ferromagnetic material using neural operators—0
Dynamic Kernel Convolution Network with Scene-dedicate Training for Sound Event Localization and Detection—0
BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification—0
Dynamic Nonlinear Mixup with Distance-based Sample Selection—0
Broad Adversarial Training with Data Augmentation in the Output Space—0
A Comprehensive Survey of Grammar Error Correction—0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation—0
Early Detection of Tuberculosis with Machine Learning Cough Audio Analysis: Towards More Accessible Global Triaging Usage—0
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation—0
Domain generalization for retinal vessel segmentation via Hessian-based vector field—0
Easy Data Augmentation in Sentiment Analysis of Cyberbullying—0
Easy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking—0
Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation—0
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models—0
Adversarial Data Augmentation via Deformation Statistics—0
Domain Generalization for In-Orbit 6D Pose Estimation—0
Domain Generalization Emerges from Dreaming—0
An overview of mixing augmentation methods and augmentation strategies—0
Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads—0
Can Deep Learning Trigger Alerts from Mobile-Captured Images?—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients—0
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning—0
Bridging the visual gap in VLN via semantically richer instructions—0
Domain Generalization: A Survey—0
A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification—0
Domain Generalization -- A Causal Perspective—0
EDF: Ensemble, Distill, and Fuse for Easy Video Labeling—0
Edges to Shapes to Concepts: Adversarial Augmentation for Robust Vision—0
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)—0
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021—0
ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models—0
EduMT: Developing Machine Translation System for Educational Content in Indian Languages—0
EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification—0
Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization—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