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:

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Papers

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Showing 6901–6950 of 8378 papers

TitleStatusHype
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
Population Based Training for Data Augmentation and Regularization in Speech Recognition—0
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language InferenceCode0
Affine-Invariant Robust Training—0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification—0
Learning to Recombine and Resample Data for Compositional GeneralizationCode0
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation—0
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation—0
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
On the Role of Supervision in Unsupervised Constituency Parsing—0
Image Translation for Medical Image Generation -- Ischemic Stroke LesionsCode0
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks—0
Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses—0
On the Effects of Knowledge-Augmented Data in Word Embeddings—0
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning—0
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?—0
Reverse Operation based Data Augmentation for Solving Math Word ProblemsCode0
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceCode0
COVID-19 Classification of X-ray Images Using Deep Neural Networks—0
3D-Aided Data Augmentation for Robust Face Understanding—0
Consensus Clustering With Unsupervised Representation Learning—0
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