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.

Further readings:

( Image credit: Albumentations )

Papers

Showing 78517900 of 8378 papers

TitleStatusHype
Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis0
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition0
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?Code0
Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation0
Deep learning framework DNN with conditional WGAN for protein solubility prediction0
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical EncoderCode0
Integrating domain knowledge: using hierarchies to improve deep classifiers0
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern ClassificationCode0
AclNet: efficient end-to-end audio classification CNN0
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks0
Deep Neural Network Augmentation: Generating Faces for Affect Analysis0
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks0
Learning data augmentation policies using augmented random searchCode0
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases0
ColorUNet: A convolutional classification approach to colorization0
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding0
Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data AugmentationCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
Texture Synthesis Guided Deep Hashing for Texture Image Retrieval0
Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning0
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora ResolutionCode0
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks0
On the End-to-End Solution to Mandarin-English Code-switching Speech RecognitionCode0
Hallucinations in neural machine translation0
Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization0
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping0
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning0
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks0
Deep Poisson gamma dynamical systems0
Training of a Skull-Stripping Neural Network with efficient data augmentationCode0
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection0
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling0
DSFD: Dual Shot Face DetectorCode0
Improving label efficiency through multi-task learning on auditory data0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition0
Deep multi-survey classification of variable stars0
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation0
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risksCode0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation0
Multi-Source Neural Machine Translation with Data Augmentation0
Ubicoustics: Plug-and-Play Acoustic Activity RecognitionCode0
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks0
Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial MachineryCode0
Efficient Augmentation via Data Subsampling0
Perfusion parameter estimation using neural networks and data augmentation0
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models0
Automatic Configuration of Deep Neural Networks with EGOCode0
Invariance Analysis of Saliency Models versus Human Gaze During Scene Free ViewingCode1
SECOND: Sparsely Embedded Convolutional DetectionCode2
Deep Geodesic Learning for Segmentation and Anatomical Landmarking0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6Unverified
5ResNet-200 (UA)Accuracy (%)80.4Unverified
6ResNet-200 (AA)Accuracy (%)80Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7Unverified
2PaCMAPClassification Accuracy85.3Unverified
3hNNEClassification Accuracy77.4Unverified
4TopoAEClassification Accuracy74.6Unverified