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 3001–3025 of 8378 papers

TitleStatusHype
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Checks and Strategies for Enabling Code-Switched Machine Translation—0
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification—0
Enhancing Spoofing Speech Detection Using Rhythm Information—0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation—0
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation—0
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN—0
Document Image Layout Analysis via Explicit Edge Embedding Network—0
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection—0
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
Do CNNs Encode Data Augmentations?—0
Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Invariance Principle Meets Vicinal Risk Minimization—0
Enlightenment Period Improving DNN Performance—0
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—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
Show:102550
← PrevPage 121 of 336Next →
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