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 6251–6300 of 8378 papers

TitleStatusHype
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching—0
LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods—0
LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation—0
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?—0
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition—0
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition—0
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI—0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs—0
Localization of Malaria Parasites and White Blood Cells in Thick Blood Smears—0
Localized Contrastive Learning on Graphs—0
Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques—0
Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting—0
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks—0
Locally Adaptive Dynamic Networks—0
Local Magnification for Data and Feature Augmentation—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Logarithmic Lenses: Exploring Log RGB Data for Image Classification—0
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text—0
Logic Guided Genetic Algorithms—0
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction—0
Longitudinal detection of new MS lesions using Deep Learning—0
Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations—0
Long-Tailed Continual Learning For Visual Food Recognition—0
Long-tailed Food Classification—0
Long-tailed Recognition by Learning from Latent Categories—0
Long-Term Modeling of Financial Machine Learning for Active Portfolio Management—0
Long Term Object Detection and Tracking in Collaborative Learning Environments—0
Long-time predictive modeling of nonlinear dynamical systems using neural networks—0
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant—0
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts—0
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations—0
Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Low-complexity deep learning frameworks for acoustic scene classification—0
Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone—0
Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification—0
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation—0
Low-rank representation of head impact kinematics: A data-driven emulator—0
Low-Resolution Neural Networks—0
Low-resource expressive text-to-speech using data augmentation—0
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System—0
Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Pretraining by Backtranslation for End-to-end ASR in Low-Resource Settings—0
Low-Resource Text-to-Speech Using Specific Data and Noise Augmentation—0
Low-Resource Vision Challenges for Foundation Models—0
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks—0
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects—0
LS-Net: Fast Single-Shot Line-Segment Detector—0
Show:102550
← PrevPage 126 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