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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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2276–2300 of 8378 papers

TitleStatusHype
ColorUNet: A convolutional classification approach to colorization—0
A Simplified Framework for Contrastive Learning for Node Representations—0
A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
Adapting Multilingual Models for Code-Mixed Translation using Back-to-Back Translation—0
ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
Does Synthetic Data Make Large Language Models More Efficient?—0
Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?—0
Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration—0
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation—0
Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer—0
IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation—0
Does Data Augmentation Lead to Positive Margin?—0
Cold Start Streaming Learning for Deep Networks—0
Does enhanced shape bias improve neural network robustness to common corruptions?—0
Cognitive Biases in Large Language Models for News Recommendation—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI—0
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation—0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition—0
A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes—0
Does equivariance matter at scale?—0
A Fourier Perspective on Model Robustness in Computer Vision—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