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:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–900 of 8378 papers

TitleStatusHype
Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data—0
Improved Multi-Task Brain Tumour Segmentation with Synthetic Data AugmentationCode2
On-Device Emoji Classifier Trained with GPT-based Data Augmentation for a Mobile Keyboard—0
DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation BenchmarkCode0
Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy—0
Self-Compositional Data Augmentation for Scientific Keyphrase GenerationCode0
PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production—0
ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing—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
NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields—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
Fair In-Context Learning via Latent Concept VariablesCode0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation—0
Online Relational Inference for Evolving Multi-agent Interacting SystemsCode0
PreCM: The Padding-based Rotation Equivariant Convolution Mode for Semantic Segmentation—0
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identificationCode0
AquaFuse: Waterbody Fusion for Physics Guided View Synthesis of Underwater Scenes—0
SANN-PSZ: Spatially Adaptive Neural Network for Head-Tracked Personal Sound Zones—0
Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment AnalysisCode0
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems—0
Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking—0
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT NetworksCode0
DiffBatt: A Diffusion Model for Battery Degradation Prediction and SynthesisCode1
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative ModelsCode0
Provable Benefit of Cutout and CutMix for Feature Learning—0
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation—0
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch InferenceCode1
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Spatiotemporal Agent Detection 2024—0
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024—0
Does equivariance matter at scale?—0
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity—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
Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models—0
FairSkin: Fair Diffusion for Skin Disease Image Generation—0
Data Generation for Hardware-Friendly Post-Training QuantizationCode3
Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification—0
LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction—0
Synthetica: Large Scale Synthetic Data for Robot Perception—0
Scaling-based Data Augmentation for Generative Models and its Theoretical Extension—0
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models—0
Mitigating Unauthorized Speech Synthesis for Voice ProtectionCode1
BongLLaMA: LLaMA for Bangla Language—0
Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector QuantizationCode0
Guiding Through Complexity: What Makes Good Supervision for Hard Reasoning Tasks?Code0
SAFE setup for generative molecular design—0
Show:102550
← PrevPage 18 of 168Next →
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