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 4101–4150 of 8378 papers

TitleStatusHype
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
RenyiCL: Contrastive Representation Learning with Skew Renyi DivergenceCode1
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation—0
Regularizing Deep Neural Networks with Stochastic Estimators of Hessian TraceCode0
HyperTime: Implicit Neural Representation for Time Series—0
Towards Sequence-Level Training for Visual TrackingCode1
Draft, Command, and Edit: Controllable Text Editing in E-Commerce—0
Domain-Specific Text Generation for Machine TranslationCode1
MixSKD: Self-Knowledge Distillation from Mixup for Image RecognitionCode1
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation—0
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource ScenariosCode1
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors—0
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging—0
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models—0
Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation—0
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and ApplicationsCode1
Exploring the Effects of Data Augmentation for Drivable Area Segmentation—0
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment AnalysisCode0
A Semantic Alignment System for Multilingual Query-Product Retrieval—0
Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks—0
Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel StatisticsCode1
Augmentation Learning for Semi-Supervised Classification—0
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain ImagesCode0
Convolutional Fine-Grained Classification with Self-Supervised Target Relation RegularizationCode1
Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative ModelsCode0
A Feature-space Multimodal Data Augmentation Technique for Text-video RetrievalCode1
SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences—0
RealPatch: A Statistical Matching Framework for Model Patching with Real SamplesCode0
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text—0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment AnalysisCode3
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction—0
Multilingual Coreference Resolution in Multiparty DialogueCode0
Few-Shot Class-Incremental Learning from an Open-Set PerspectiveCode1
PolarMix: A General Data Augmentation Technique for LiDAR Point CloudsCode2
A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation—0
SimCURL: Simple Contrastive User Representation Learning from Command Sequences—0
Robust Trajectory Prediction against Adversarial Attacks—0
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation—0
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval—0
Self-Supervised Hypergraph Transformer for Recommender SystemsCode1
Efficient Training of Language Models to Fill in the MiddleCode2
Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant argumentsCode0
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods—0
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
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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