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 3051–3100 of 8378 papers

TitleStatusHype
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
Equivariant Neural Tangent Kernels—0
Equivariant score-based generative models provably learn distributions with symmetries efficiently—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing—0
Document Image Layout Analysis via Explicit Edge Embedding Network—0
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Do CNNs Encode Data Augmentations?—0
Estimating Input Coefficients for Regional Input-Output Tables Using Deep Learning with Mixup—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach—0
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation—0
Finetuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition—0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts—0
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference—0
DMix: Distance Constrained Interpolative Mixup—0
DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation—0
DM-CT: Consistency Training with Data and Model Perturbation—0
Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images—0
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification—0
Evaluating Deep Music Generation Methods Using Data Augmentation—0
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation—0
Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
Evaluating Neural Networks for Early Maritime Threat Detection—0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples—0
Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5—0
Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces—0
Evaluating the Impact of Data Augmentation on Predictive Model Performance—0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing—0
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evaluation Metrics for Text Data Augmentation in NLP—0
Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness—0
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models—0
Evaluation of generative networks through their data augmentation capacity—0
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies—0
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training—0
Show:102550
← PrevPage 62 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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified