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 7901–7950 of 8378 papers

TitleStatusHype
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
Texture Synthesis Guided Deep Hashing for Texture Image Retrieval—0
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora ResolutionCode0
Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning—0
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks—0
On the End-to-End Solution to Mandarin-English Code-switching Speech RecognitionCode0
Hallucinations in neural machine translation—0
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping—0
Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization—0
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning—0
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks—0
Deep Poisson gamma dynamical systems—0
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection—0
Training of a Skull-Stripping Neural Network with efficient data augmentationCode0
DSFD: Dual Shot Face DetectorCode0
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling—0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition—0
Improving label efficiency through multi-task learning on auditory data—0
Deep multi-survey classification of variable stars—0
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risksCode0
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Multi-Source Neural Machine Translation with Data Augmentation—0
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks—0
Ubicoustics: Plug-and-Play Acoustic Activity RecognitionCode0
Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial MachineryCode0
Efficient Augmentation via Data Subsampling—0
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models—0
Perfusion parameter estimation using neural networks and data augmentation—0
Automatic Configuration of Deep Neural Networks with EGOCode0
Deep Geodesic Learning for Segmentation and Anatomical Landmarking—0
Transfer Incremental Learning using Data Augmentation—0
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Data Augmentation for Neural Online Chats Response Selection—0
IPS-WASEDA system at CoNLL--SIGMORPHON 2018 Shared Task on morphological inflection—0
T\"ubingen-Oslo system at SIGMORPHON shared task on morphological inflection. A multi-tasking multilingual sequence to sequence model.—0
Learning Text Representations for 500K Classification Tasks on Named Entity DisambiguationCode0
The MLLP-UPV German-English Machine Translation System for WMT18—0
Tencent Neural Machine Translation Systems for WMT18—0
Sheffield Submissions for WMT18 Multimodal Translation Shared Task—0
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation—0
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images—0
Theoretical and Empirical Study of Adversarial Examples—0
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information—0
Hallucinations in Neural Machine Translation—0
Unsupervised Adversarial Invariance—0
Flexible Mixture Modeling on Constrained Spaces—0
Adversarial Defense via Data Dependent Activation Function and Total Variation MinimizationCode0
3D Human Pose Estimation with Siamese Equivariant EmbeddingCode0
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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