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 3951–4000 of 8378 papers

TitleStatusHype
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
Data augmentation and image understanding—0
Hypernetwork-Based Augmentation—0
Hyperspectral CNN Classification with Limited Training Samples—0
Hyperspectral Data Augmentation—0
Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation—0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing—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
Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation—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
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation—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
Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method—0
IDA: Informed Domain Adaptive Semantic Segmentation—0
Angle Range and Identity Similarity Enhanced Gaze and Head Redirection based on Synthetic data—0
Advancing Recycling Efficiency: A Comparative Analysis of Deep Learning Models in Waste Classification—0
Diabetic retinopathy image classification method based on GreenBen data augmentation—0
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning—0
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction—0
Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning—0
Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN—0
Diabetes detection using deep learning techniques with oversampling and feature augmentation—0
BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting—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
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning—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
An Exploration of Data Augmentation and RNN Architectures for Question Ranking in Community Question Answering—0
Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—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
Image-based Deep Learning for Smart Digital Twins: a Review—0
BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—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