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 2901–2950 of 8378 papers

TitleStatusHype
Encoding Power Traces as Images for Efficient Side-Channel Analysis—0
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization—0
Facebook AI’s WMT20 News Translation Task Submission—0
End-to-End Action Segmentation Transformer—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Adversarial Data Augmentation for Robust Speaker Verification—0
End-to-end neural networks for subvocal speech recognition—0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification—0
End-to-End Speech Recognition with High-Frame-Rate Features Extraction—0
End-to-End Speech Translation of Arabic to English Broadcast News—0
End-to-end Speech Translation System Description of LIT for IWSLT 2019—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Extrinsic Factors Affecting the Accuracy of Biomedical NER—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?—0
Does Synthetic Data Make Large Language Models More Efficient?—0
Bridging Domain Gap for Flight-Ready Spaceborne Vision—0
Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation—0
Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?—0
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals—0
CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors—0
Enhanced Image Classification With Data Augmentation Using Position Coordinates—0
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification—0
Are conditional GANs explicitly conditional?—0
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics—0
Enhanced Offensive Language Detection Through Data Augmentation—0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks—0
EyeBAG: Accurate Control of Eye Blink and Gaze Based on Data Augmentation Leveraging Style Mixing—0
Enhanced Transformer Model for Data-to-Text Generation—0
Face Emotion Recognization Using Dataset Augmentation Based on Neural Network—0
Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation—0
Does equivariance matter at scale?—0
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling—0
Does enhanced shape bias improve neural network robustness to common corruptions?—0
Does Data Augmentation Lead to Positive Margin?—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation—0
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI—0
A novel method to enhance pneumonia detection via a model-level ensembling of CNN and vision transformer—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