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.

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( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1575 of 8378 papers

TitleStatusHype
NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning—0
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection—0
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks—0
Saturn: Sample-efficient Generative Molecular Design using Memory ManipulationCode2
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
DualContrast: Unsupervised Disentangling of Content and Transformations with Implicit Parameterization—0
GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement LearningCode1
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction—0
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time SeriesCode1
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification—0
Planted: a dataset for planted forest identification from multi-satellite time series—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
Combining Euclidean Alignment and Data Augmentation for BCI decoding—0
Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Data Augmentation Techniques for Process Extraction from Scientific Publications—0
Can LLMs Solve longer Math Word Problems Better?Code0
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images—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