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 4551–4600 of 8378 papers

TitleStatusHype
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification—0
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning—0
Pseudo-labeling for Scalable 3D Object Detection—0
Pseudo-labelling Enhanced Media Bias Detection—0
Pseudo-Non-Linear Data Augmentation via Energy Minimization—0
Pseudo-positive regularization for deep person re-identification—0
Pseudo-Representation Labeling Semi-Supervised Learning—0
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction—0
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models—0
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection—0
Pushing Boundaries: Mixup's Influence on Neural Collapse—0
Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering—0
Pushing the limits of self-supervised speaker verification using regularized distillation framework—0
PV-faultNet: Optimized CNN Architecture to detect defects resulting efficient PV production—0
PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks—0
Pyramid Focusing Network for mutation prediction and classification in CT images—0
QA Domain Adaptation using Data Augmentation and Contrastive Adaptation—0
QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation—0
QA-TOOLBOX: Conversational Question-Answering for process task guidance in manufacturing—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
QGAN-based data augmentation for hybrid quantum-classical neural networks—0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning—0
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions—0
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task—0
Qualitative Data Augmentation for Performance Prediction in VLSI circuits—0
Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment—0
Quality-aware semi-supervised learning for CMR segmentation—0
Quantifying Data Augmentation for LiDAR based 3D Object Detection—0
Quantifying Human Bias and Knowledge to guide ML models during Training—0
Quantifying Overfitting: Introducing the Overfitting Index—0
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting—0
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology—0
Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation—0
Quantifying Translation-Invariance in Convolutional Neural Networks—0
Quantum Adversarial Learning for Kernel Methods—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Quantum-inspired Representation for Long-tail Senses of Word Sense Disambiguation—0
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation—0
Query-oriented Data Augmentation for Session Search—0
QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time—0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception—0
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation—0
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather—0
Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices—0
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images—0
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection—0
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes—0
Raman spectral analysis of mixtures with one-dimensional convolutional neural network—0
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios—0
Randomized Dimension Reduction with Statistical Guarantees—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