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 901–950 of 8378 papers

TitleStatusHype
Your Image is Secretly the Last Frame of a Pseudo Video—0
SAFE setup for generative molecular design—0
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCoCode0
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches—0
On Occlusions in Video Action Detection: Benchmark Datasets And Training RecipesCode0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Improving Model Factuality with Fine-grained Critique-based Evaluator—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts—0
CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7Code0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Enriching GNNs with Text Contextual Representations for Detecting Disinformation Campaigns on Social MediaCode0
Ali-AUG: Innovative Approaches to Labeled Data Augmentation using One-Step Diffusion Model—0
GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformation—0
Together We Can: Multilingual Automatic Post-Editing for Low-Resource LanguagesCode0
Data Augmentation for Automated Adaptive Rodent Training—0
Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment—0
Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression RecognitionCode0
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive LearningCode0
Scalable Implicit Graphon LearningCode0
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation—0
Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
SaVe-TAG: Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs—0
CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare—0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing ImagesCode0
Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence—0
Efficient Neural Network Training via Subset Pretraining—0
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics—0
ToW: Thoughts of Words Improve Reasoning in Large Language ModelsCode0
Generalizing Motion Planners with Mixture of Experts for Autonomous DrivingCode3
Deep Learning and Data Augmentation for Detecting Self-Admitted Technical DebtCode0
Towards Combating Frequency Simplicity-biased Learning for Domain GeneralizationCode0
Habaek: High-performance water segmentation through dataset expansion and inductive bias optimizationCode0
An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection—0
KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement—0
Data Augmentation via Diffusion Model to Enhance AI Fairness—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks—0
Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models—0
A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models—0
How Does Data Diversity Shape the Weight Landscape of Neural Networks?—0
Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image SegmentationCode1
Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data AugmentationCode0
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation—0
STCON System for the CHiME-8 Challenge—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