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 4351–4400 of 8378 papers

TitleStatusHype
One-Shot Segmentation of Novel White Matter Tracts via Extensive Data AugmentationCode0
Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study—0
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network—0
Modality-Agnostic Debiasing for Single Domain Generalization—0
PointPatchMix: Point Cloud Mixing with Patch Scoring—0
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning—0
Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and RecognitionCode0
DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation ProblemCode0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
Retinal Image Segmentation with Small Datasets—0
Pedestrian Attribute Editing for Gait Recognition and Anonymization—0
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples—0
An Improved Data Augmentation Scheme for Model Predictive Control Policy Approximation—0
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment—0
Rethinking Range View Representation for LiDAR Segmentation—0
Structural Similarity: When to Use Deep Generative Models on Imbalanced Image Dataset Augmentation—0
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells—0
PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge DistillationCode0
On the Implicit Bias of Linear Equivariant Steerable Networks—0
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data—0
IDA: Informed Domain Adaptive Semantic Segmentation—0
WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks—0
A Study of Augmentation Methods for Handwritten Stenography Recognition—0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Lightweight, Uncertainty-Aware Conformalized Visual Odometry—0
Spoof Trace Disentanglement for generic face antispoofing—0
Exploring Data Augmentation Methods on Social Media Corpora—0
Bayesian CART models for insurance claims frequency—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Unproportional mosaicing—0
Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators in Neural NetworksCode0
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Evolutionary Augmentation Policy Optimization for Self-supervised Learning—0
A Vision for Semantically Enriched Data Science—0
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition—0
FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling—0
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation—0
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain—0
An Effective Crop-Paste Pipeline for Few-shot Object Detection—0
Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation—0
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets—0
Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAMCode0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit—0
Soft labelling for semantic segmentation: Bringing coherence to label down-samplingCode0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
AugGPT: Leveraging ChatGPT for Text Data AugmentationCode0
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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