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 4551–4575 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
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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