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 3551–3600 of 8378 papers

TitleStatusHype
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
Portuguese FAQ for Financial Services—0
Estimating Uncertainty in Landslide Segmentation Models—0
Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Domain Generalization of 3D Object Detection by Density-ResamplingCode0
Automated Detection of hidden Damages and Impurities in Aluminum Die Casting Materials and Fibre-Metal Laminates using Low-quality X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning—0
Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts—0
Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction TuningCode0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
SegMix: A Simple Structure-Aware Data Augmentation Method—0
Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production—0
Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual SpeechCode0
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations—0
An Empathetic User-Centric Chatbot for Emotional Support—0
Language Semantic Graph Guided Data-Efficient LearningCode0
Data Augmentations in Deep Weight Spaces—0
On the Calibration of Multilingual Question Answering LLMs—0
Physical Adversarial Examples for Multi-Camera Systems—0
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals—0
Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation—0
Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition—0
Histopathologic Cancer DetectionCode0
A Study of Implicit Ranking Unfairness in Large Language ModelsCode0
Masked Face Dataset Generation and Masked Face RecognitionCode0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Anchor Data AugmentationCode0
Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis—0
Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation—0
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks—0
Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking—0
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree TransformationCode0
FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment—0
Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples—0
Social Media Bot Detection using Dropout-GAN—0
3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer—0
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition—0
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Lie Point Symmetry and Physics Informed Networks—0
Modelling Sentiment Analysis: LLMs and data augmentation techniquesCode0
Improving the Effectiveness of Deep Generative Data—0
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase—0
Co-training and Co-distillation for Quality Improvement and Compression of Language Models—0
Spoken Dialogue System for Medical Prescription Acquisition on Smartphone: Development, Corpus and Evaluation—0
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification—0
Model-based Counterfactual Generator for Gender Bias Mitigation—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