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

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Showing 6926–6950 of 8378 papers

TitleStatusHype
WeMix: How to Better Utilize Data Augmentation—0
DecAug: Augmenting HOI Detection via Decomposition—0
Training Data Augmentation for Deep Learning Radio Frequency Systems—0
Masked Face Recognition with Latent Part Detection—0
Understanding tables with intermediate pre-training—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data—0
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation—0
Data augmentation as stochastic optimization—0
Data Instance Prior for Transfer Learning in GANs—0
Medical Image Segmentation Using Deep Learning: A SurveyCode0
Recognition and Synthesis of Object Transport Motion—0
Empirical Study of Text Augmentation on Social Media Text in VietnameseCode0
A little goes a long way: Improving toxic language classification despite data scarcityCode0
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset—0
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
On Data Augmentation for Extreme Multi-label Classification—0
GraphCrop: Subgraph Cropping for Graph Classification—0
TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
Encoding Robustness to Image Style via Adversarial Feature PerturbationsCode0
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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