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 8076–8100 of 8378 papers

TitleStatusHype
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression—0
Layer-Parallel Training of Residual Networks with Auxiliary Variables—0
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks—0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition—0
Leaf Identification Using a Deep Convolutional Neural Network—0
Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition—0
Learnability and Expressiveness in Self-Supervised Learning—0
Learnable Gabor modulated complex-valued networks for orientation robustness—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Learn Beneficial Noise as Graph Augmentation—0
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification—0
Learned versus Hand-Designed Feature Representations for 3d Agglomeration—0
Learning 3D Object Categories by Looking Around Them—0
Learning a 3D descriptor for cross-source point cloud registration from synthetic data—0
Learning a Facial Expression Embedding Disentangled From Identity—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
Learning and Retrieval from Prior Data for Skill-based Imitation Learning—0
Learning Augmentation Network via Influence Functions—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond—0
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Learning-Based TSP-Solvers Tend to Be Overly Greedy—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