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 7651–7700 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
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning—0
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning—0
ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents—0
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge—0
Icospherical Chemical Objects (ICOs) allow for chemical data augmentation and maintain rotational, translation and permutation invariance—0
IDA: Informed Domain Adaptive Semantic Segmentation—0
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations—0
Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network—0
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning—0
Identifying Aggression and Toxicity in Comments using Capsule Network—0
Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning—0
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces—0
iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting—0
"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition—0
IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News Similarity—0
IIITN NLP at SMM4H 2021 Tasks: Transformer Models for Classification on Health-Related Imbalanced Twitter Datasets—0
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition—0
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes—0
Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM—0
Image augmentation improves few-shot classification performance in plant disease recognition—0
Image augmentation with conformal mappings for a convolutional neural network—0
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling?—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified