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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2351–2400 of 8378 papers

TitleStatusHype
Dynamic Gesture Recognition—0
Dynamic hysteresis model of grain-oriented ferromagnetic material using neural operators—0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading—0
Affine transformation estimation improves visual self-supervised learning—0
A Semantic Alignment System for Multilingual Query-Product Retrieval—0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets—0
Dynamic Feature Learning and Matching for Class-Incremental Learning—0
Dynamic Kernel Convolution Network with Scene-dedicate Training for Sound Event Localization and Detection—0
Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation—0
A Self-Training Method for Semi-Supervised GANs—0
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift—0
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs—0
Affine-Invariant Robust Training—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
Affine Disentangled GAN for Interpretable and Robust AV Perception—0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
A scoping review of transfer learning research on medical image analysis using ImageNet—0
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction—0
Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model—0
Dynamic Motion Blending for Versatile Motion Editing—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
Clean Evaluations on Contaminated Visual Language Models—0
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
AFFACT - Alignment-Free Facial Attribute Classification Technique—0
Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification—0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
AFEN: Respiratory Disease Classification using Ensemble Learning—0
AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning—0
Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy—0
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
Classifying cow stall numbers using YOLO—0
Classifying COVID-19 vaccine narratives—0
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network—0
ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers—0
ROI Regularization for Semi-supervised and Supervised Learning—0
Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning—0
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks—0
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics—0
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems—0
Dynamic Batch Norm Statistics Update for Natural Robustness—0
Dynamic Nonlinear Mixup with Distance-based Sample Selection—0
Effective Data Augmentation with Multi-Domain Learning GANs—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