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 5001–5050 of 8378 papers

TitleStatusHype
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks—0
A Semantic Alignment System for Multilingual Query-Product Retrieval—0
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment AnalysisCode0
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
Augmentation Learning for Semi-Supervised Classification—0
Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative ModelsCode0
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain ImagesCode0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction—0
Multilingual Coreference Resolution in Multiparty DialogueCode0
A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance—0
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval—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
Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
SimCURL: Simple Contrastive User Representation Learning from Command Sequences—0
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
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network—0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety—0
Controllable User Dialogue Act Augmentation for Dialogue State TrackingCode0
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition—0
Transplantation of Conversational Speaking Style with Interjections in Sequence-to-Sequence Speech Synthesis—0
Efficient Classification with Counterfactual Reasoning and Active LearningCode0
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments—0
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation—0
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence—0
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks—0
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture—0
Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation—0
SplitMixer: Fat Trimmed From MLP-like ModelsCode0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Improving Data Driven Inverse Text Normalization using Data Augmentation—0
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup—0
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation—0
Revisiting data augmentation for subspace clustering—0
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection—0
On the Usability of Transformers-based models for a French Question-Answering task—0
Classifying COVID-19 vaccine narratives—0
Research Trends and Applications of Data Augmentation AlgorithmsCode0
WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification—0
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning—0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma ClassificationCode0
Classification of Bark Beetle-Induced Forest Tree Mortality using Deep LearningCode0
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement LearningCode0
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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