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 3201–3250 of 8378 papers

TitleStatusHype
TRG-Net: An Interpretable and Controllable Rain GeneratorCode0
SOMson -- Sonification of Multidimensional Data in Kohonen Maps—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack—0
Leveraging Foundation Model Automatic Data Augmentation Strategies and Skeletal Points for Hands Action Recognition in Industrial Assembly Lines—0
Don't Judge by the Look: Towards Motion Coherent Video RepresentationCode0
Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation—0
Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound ImagesCode0
IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-LabelsCode0
VIGFace: Virtual Identity Generation for Privacy-Free Face RecognitionCode0
Improved Image-based Pose Regressor Models for Underwater Environments—0
Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?—0
Boosting Disfluency Detection with Large Language Model as Disfluency GeneratorCode0
METER: a mobile vision transformer architecture for monocular depth estimationCode0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTICode0
Data Augmentation in Human-Centric Vision—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Disentangling Policy from Offline Task Representation Learning via Adversarial Data AugmentationCode0
Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization—0
Class Imbalance in Object Detection: An Experimental Diagnosis and Study of Mitigation StrategiesCode0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content RepresentationsCode0
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack—0
Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning—0
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts—0
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
PIPsUS: Self-Supervised Dense Point Tracking in UltrasoundCode0
Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networks—0
A data-centric approach to class-specific bias in image data augmentation—0
Low-Resource Court Judgment Summarization for Common Law SystemsCode0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
ECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain Adaptive Semantic SegmentationCode0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Emergent Equivariance in Deep Ensembles—0
Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types—0
False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy—0
AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation—0
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges—0
A Generative Model of Symmetry TransformationsCode0
AS-ES Learning: Towards Efficient CoT Learning in Small Models—0
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation—0
Self-Supervised Representation Learning with Meta Comprehensive Regularization—0
Multi-level Product Category Prediction through Text ClassificationCode0
Fine Tuning vs. Retrieval Augmented Generation for Less Popular KnowledgeCode0
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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