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 3701–3750 of 8378 papers

TitleStatusHype
Gibbs Max-margin Topic Models with Data Augmentation—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Global and Local Information Based Deep Network for Skin Lesion Segmentation—0
Global Context Is All You Need for Parallel Efficient Tractography Parcellation—0
A Survey on Semantics in Automated Data Science—0
aiai at the FinSBD-2 Task: Sentence, list and Item Boundary Detection and Items classification of Financial Texts Using Data Augmentation and Attention—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
Global Mixup: Eliminating Ambiguity with Clustering Relationships—0
Global Mixup: Eliminating Ambiguity with Clustering—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
Contrastive-mixup learning for improved speaker verification—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
A Survey on SAR ship classification using Deep Learning—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
Further advantages of data augmentation on convolutional neural networks—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Contrastive Learning with Negative Sampling Correction—0
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Functional Space Analysis of Local GAN Convergence—0
Fully Test-time Adaptation for Tabular Data—0
Gophormer: Ego-Graph Transformer for Node Classification—0
Go Small and Similar: A Simple Output Decay Brings Better Performance—0
A Survey on Neural Architecture Search—0
Fully Bayesian inference for neural models with negative-binomial spiking—0
Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images—0
Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives—0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
GradMix for nuclei segmentation and classification in imbalanced pathology image datasets—0
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks—0
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences—0
Full-Frame Scene Coordinate Regression for Image-Based Localization—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Learning from Pairwise Measurements—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
A Survey on Face Data Augmentation—0
Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation—0
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization—0
GraphCrop: Subgraph Cropping for Graph Classification—0
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis—0
FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—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