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

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Showing 3451–3475 of 8378 papers

TitleStatusHype
FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection—0
FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation—0
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
Discriminative Relational Topic Models—0
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—0
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification—0
Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction—0
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization—0
Gradient-based Data Augmentation for Semi-Supervised Learning—0
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips—0
FROTE: Feedback Rule-Driven Oversampling for Editing Models—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension—0
Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction—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