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 3851–3900 of 8378 papers

TitleStatusHype
An Improved Deep Learning Approach For Product Recognition on Racks in Retail Stores—0
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning—0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Hierarchical Scene Coordinate Classification and Regression for Visual Localization—0
Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network—0
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Hierarchical Topic Presence Models—0
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity—0
Cutting-Splicing data augmentation: A novel technology for medical image segmentation—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
An Improved Data Augmentation Scheme for Model Predictive Control Policy Approximation—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Cap2Aug: Caption guided Image to Image data Augmentation—0
BongLLaMA: LLaMA for Bangla Language—0
High performing ensemble of convolutional neural networks for insect pest image detection—0
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
High-Resolution UAV Image Generation for Sorghum Panicle Detection—0
HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models—0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
Cyclic Test Time Augmentation with Entropy Weight Method—0
BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach—0
Advancing Seq2seq with Joint Paraphrase Learning—0
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation—0
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection—0
Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data—0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems—0
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation—0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs—0
BloomVQA: Assessing Hierarchical Multi-modal Comprehension—0
DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021—0
How Does Data Diversity Shape the Weight Landscape of Neural Networks?—0
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?—0
How Does Mixup Help With Robustness and Generalization?—0
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions—0
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching—0
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations—0
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?—0
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks—0
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge—0
Blocks2World: Controlling Realistic Scenes with Editable Primitives—0
How many labeled license plates are needed?—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Improving Conditioning in Context-Aware Sequence to Sequence Models—0
Show:102550
← PrevPage 78 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