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 5201–5250 of 8378 papers

TitleStatusHype
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
A Multi-LLM Debiasing Framework—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
Completely Weakly Supervised Class-Incremental Learning for Semantic Segmentation—0
Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges—0
Reconstructing Syllable Sequences in Abugida Scripts with Incomplete Inputs—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model—0
IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation—0
PixCell: A generative foundation model for digital histopathology images—0
HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge—0
3D-Aided Data Augmentation for Robust Face Understanding—0
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation—0
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies—0
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images—0
3D Data Augmentation for Driving Scenes on Camera—0
3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement—0
3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer—0
3D G-CNNs for Pulmonary Nodule Detection—0
3d human motion generation from the text via gesture action classification and the autoregressive model—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
3D Pose Regression using Convolutional Neural Networks—0
3D Rendering Framework for Data Augmentation in Optical Character Recognition—0
3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies—0
3D Skeleton-Based Action Recognition: A Review—0
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations—0
3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
A2Log: Attentive Augmented Log Anomaly Detection—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
A Bayesian Approach to Invariant Deep Neural Networks—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction—0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy—0
A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes—0
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
Abstract Text Summarization: A Low Resource Challenge—0
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models—0
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
A Car Model Identification System for Streamlining the Automobile Sales Process—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
A Causal View on Robustness of Neural Networks—0
Accelerated Neural Network Training with Rooted Logistic Objectives—0
Accelerating Ensemble Error Bar Prediction with Single Models Fits—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
Show:102550
← PrevPage 105 of 168Next →
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