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 2351–2400 of 8378 papers

TitleStatusHype
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation—0
Emotion Classification of Children Expressions—0
Isometric Transformations for Image Augmentation in Mueller Matrix PolarimetryCode0
Artificial Intelligence for Biomedical Video GenerationCode0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
A Quality-Centric Framework for Generic Deepfake Detection—0
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction—0
Impact of Label Noise on Learning Complex Features—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
GASE: Generatively Augmented Sentence Encoding—0
Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data—0
On-Device Emoji Classifier Trained with GPT-based Data Augmentation for a Mobile Keyboard—0
Self-Compositional Data Augmentation for Scientific Keyphrase GenerationCode0
Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy—0
PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production—0
Advancing Recycling Efficiency: A Comparative Analysis of Deep Learning Models in Waste Classification—0
Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting—0
DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation BenchmarkCode0
ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning—0
Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario—0
NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields—0
Fair In-Context Learning via Latent Concept VariablesCode0
Online Relational Inference for Evolving Multi-agent Interacting SystemsCode0
Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation—0
PreCM: The Padding-based Rotation Equivariant Convolution Mode for Semantic Segmentation—0
AquaFuse: Waterbody Fusion for Physics Guided View Synthesis of Underwater Scenes—0
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identificationCode0
Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment AnalysisCode0
SANN-PSZ: Spatially Adaptive Neural Network for Head-Tracked Personal Sound Zones—0
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems—0
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT NetworksCode0
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative ModelsCode0
Provable Benefit of Cutout and CutMix for Feature Learning—0
Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024—0
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Spatiotemporal Agent Detection 2024—0
Does equivariance matter at scale?—0
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms—0
Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models—0
NetAurHPD: Network Auralization Hyperlink Prediction Model to Identify Metabolic Pathways from Metabolomics DataCode0
Guided Diffusion-based Counterfactual Augmentation for Robust Session-based Recommendation—0
FairSkin: Fair Diffusion for Skin Disease Image Generation—0
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity—0
LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction—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