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 7651–7675 of 8378 papers

TitleStatusHype
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration—0
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements—0
Human Pose Transfer with Augmented Disentangled Feature Consistency—0
Human Vocal Sentiment Analysis—0
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task—0
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning—0
Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition—0
Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006—0
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction—0
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification—0
Hydranet: Data Augmentation for Regression Neural Networks—0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
Hypernetwork-Based Augmentation—0
Hyperspectral CNN Classification with Limited Training Samples—0
Hyperspectral Data Augmentation—0
HyperTime: Implicit Neural Representation for Time Series—0
I2C at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques—0
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation—0
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance—0
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance—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