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 6201–6250 of 8378 papers

TitleStatusHype
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D CavitiesCode0
DaCy: A Unified Framework for Danish NLP—0
Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
Functional Magnetic Resonance Imaging data augmentation through conditional ICACode0
BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading—0
DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021—0
PRGAN: Personalized Recommendation with Conditional Generative Adversarial NetworksCode0
Noisy Training Improves E2E ASR for the Edge—0
Seven Basic Expression Recognition Using ResNet-18—0
Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data—0
Elastic deformation of optical coherence tomography images of diabetic macular edema for deep-learning models training: how far to go?—0
A Survey of Uncertainty in Deep Neural Networks—0
GAN-based Data Augmentation for Chest X-ray Classification—0
A Survey on Data Augmentation for Text Classification—0
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal DataCode0
Exploiting Single-Channel Speech For Multi-channel End-to-end Speech Recognition—0
Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa—0
FloorLevel-Net: Recognizing Floor-Level Lines with Height-Attention-Guided Multi-task Learning—0
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
MixStyle Neural Networks for Domain Generalization and Adaptation—0
Isotonic Data Augmentation for Knowledge Distillation—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Solving Machine Learning ProblemsCode0
Supervised Contrastive Learning for Accented Speech Recognition—0
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors—0
Zero-pronoun Data Augmentation for Japanese-to-English Translation—0
The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021—0
Knowledge Distillation for Quality EstimationCode0
Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniquesCode0
Context-Aware Attention-Based Data Augmentation for POI Recommendation—0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space—0
On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned—0
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning—0
Are conditional GANs explicitly conditional?—0
Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge GraphsCode0
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams—0
Decomposed Mutual Information Estimation for Contrastive Representation Learning—0
Countering Adversarial Examples: Combining Input Transformation and Noisy Training—0
A Picture May Be Worth a Hundred Words for Visual Question Answering—0
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers—0
Partially fake it till you make it: mixing real and fake thermal images for improved object detection—0
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals—0
On the (Un-)Avoidability of Adversarial Examples—0
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image—0
ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and DissertationsCode0
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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