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.

Further readings:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3526–3550 of 8378 papers

TitleStatusHype
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction—0
HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models—0
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension—0
GASE: Generatively Augmented Sentence Encoding—0
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models—0
Hallucinations in neural machine translation—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications—0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
GCC: Generative Color Constancy via Diffusing a Color Checker—0
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI—0
FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space—0
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks—0
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation—0
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Discrete Latent Perspective Learning for Segmentation and Detection—0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Show:102550
← PrevPage 142 of 336Next →
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