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

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Showing 4651–4700 of 8378 papers

TitleStatusHype
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Coarse-to-fine Task-driven Inpainting for Geoscience Images—0
Deep Learning on a Healthy Data Diet: Finding Important Examples for FairnessCode0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process—0
Simple and Effective Augmentation Methods for CSI Based Indoor Localization—0
Quantifying Human Bias and Knowledge to guide ML models during Training—0
Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets—0
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning—0
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering—0
3d human motion generation from the text via gesture action classification and the autoregressive model—0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation—0
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation—0
Semantic keypoint extraction for scanned animals using multi-depth-camera systemsCode0
Learning unfolded networks with a cyclic group structureCode0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra—0
TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task—0
Consecutive Question Generation via Dynamic Multitask Learning—0
Local Magnification for Data and Feature Augmentation—0
Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation—0
Persian Emotion Detection using ParsBERT and Imbalanced Data Handling ApproachesCode0
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network—0
CardiacGen: A Hierarchical Deep Generative Model for Cardiac SignalsCode0
DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography—0
Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns—0
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems—0
A deep learning framework to generate realistic population and mobility data—0
Robustifying Deep Vision Models Through Shape Sensitization—0
Hand gesture recognition using 802.11ad mmWave sensor in the mobile device—0
Boosting Semi-Supervised 3D Object Detection with Semi-SamplingCode0
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement—0
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization—0
Textual Data Augmentation for Patient Outcomes Prediction—0
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement—0
Age Prediction Performance Varies Across Deep, Superficial, and Cerebellar White Matter Connections—0
Masked Contrastive Representation Learning—0
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer DiagnosisCode0
Equivariant Contrastive Learning for Sequential RecommendationCode0
Impact of Adversarial Training on Robustness and Generalizability of Language Models—0
Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy—0
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition—0
Extending Temporal Data Augmentation for Video Action Recognition—0
Cold Start Streaming Learning for Deep Networks—0
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images—0
Training self-supervised peptide sequence models on artificially chopped proteins—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