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 2001–2050 of 8378 papers

TitleStatusHype
Physics-Informed Gradient Estimation for Accelerating Deep Learning based AC-OPF—0
Steganographic Embeddings as an Effective Data AugmentationCode0
Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation—0
Mixup Model Merge: Enhancing Model Merging Performance through Randomized Linear InterpolationCode0
Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration—0
Reducing false positives in strong lens detection through effective augmentation and ensemble learning—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation—0
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation—0
Image compositing is all you need for data augmentation—0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
Fake It Till You Make It: Using Synthetic Data and Domain Knowledge for Improved Text-Based Learning for LGE Detection—0
Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks—0
Diversity-Oriented Data Augmentation with Large Language Models—0
Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarcity—0
SpeechT: Findings of the First Mentorship in Speech Translation—0
AudioSpa: Spatializing Sound Events with Text—0
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment—0
Generating Skyline Datasets for Data Science Models—0
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids—0
A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency RegularizationCode0
Adaptive Neural Networks for Intelligent Data-Driven Development—0
A Robust Attack: Displacement Backdoor Attack—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion—0
Towards Understanding Why Data Augmentation Improves Generalization—0
Generalizability through Explainability: Countering Overfitting with Counterfactual Examples—0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Low-Resolution Neural Networks—0
Foliar Uptake of Biocides: Statistical Assessment of Compartmental and Diffusion-Based Models—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Robustly Learning Monotone Generalized Linear Models via Data Augmentation—0
Data Augmentation to Improve Large Language Models in Food Hazard and Product DetectionCode0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Towards Understanding of Frequency Dependence on Sound Event Detection—0
An Advanced NLP Framework for Automated Medical Diagnosis with DeBERTa and Dynamic Contextual Positional Gating—0
Data Augmentation and Regularization for Learning Group EquivarianceCode0
A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images—0
Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation—0
Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority GenerationCode0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch—0
DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability—0
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation—0
NextBestPath: Efficient 3D Mapping of Unseen Environments—0
Importance Sampling via Score-based Generative Models—0
Graph Contrastive Learning for Connectome ClassificationCode0
YOLOv4: A Breakthrough in Real-Time Object Detection—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