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 4951–5000 of 8378 papers

TitleStatusHype
Visual Speech Recognition in a Driver Assistance System—0
Radial Prediction Domain Adaption Classifier for the MIDOG 2022 ChallengeCode0
Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature FusionCode0
Synthetic Latent Fingerprint Generator—0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Object Goal Navigation using Data Regularized Q-Learning—0
Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfer—0
Multi tasks RetinaNet for mitosis detection—0
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
Data Augmentation for Graph Data: Recent Advancements—0
Image augmentation improves few-shot classification performance in plant disease recognition—0
A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)—0
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation—0
Improving Natural-Language-based Audio Retrieval with Transfer Learning and Audio & Text Augmentations—0
Unsupervised Question Answering via Answer DiversifyingCode0
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution—0
Data augmentation on graphs for table type classificationCode0
Learning linear modules in a dynamic network with missing node observations—0
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels—0
Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models—0
RAIN: RegulArization on Input and Network for Black-Box Domain AdaptationCode0
Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron—0
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue GenerationCode0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data AugmentationCode0
Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models—0
On the Privacy Effect of Data Enhancement via the Lens of MemorizationCode0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of SuccessCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
Online 3D Bin Packing Reinforcement Learning Solution with Buffer—0
Syntax-driven Data Augmentation for Named Entity RecognitionCode0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
The Causal Structure of Domain Invariant Supervised Representation Learning—0
Gradient Mask: Lateral Inhibition Mechanism Improves Performance in Artificial Neural Networks—0
Enhancing Graph Contrastive Learning with Node Similarity—0
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation—0
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
HyperTime: Implicit Neural Representation for Time Series—0
Draft, Command, and Edit: Controllable Text Editing in E-Commerce—0
Regularizing Deep Neural Networks with Stochastic Estimators of Hessian TraceCode0
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation—0
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors—0
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models—0
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging—0
Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation—0
Exploring the Effects of Data Augmentation for Drivable Area Segmentation—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder—0
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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