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 8001–8050 of 8378 papers

TitleStatusHype
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
基于字词粒度噪声数据增强的中文语法纠错(Chinese Grammatical Error Correction enhanced by Data Augmentation from Word and Character Levels)—0
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment—0
Joining datasets via data augmentation in the label space for neural networks—0
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds—0
Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning—0
Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Joint Learning of Generative Translator and Classifier for Visually Similar Classes—0
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation—0
Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation—0
Joint Search of Data Augmentation Policies and Network Architectures—0
Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents—0
Joint Speaker Encoder and Neural Back-end Model for Fully End-to-End Automatic Speaker Verification with Multiple Enrollment Utterances—0
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data—0
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System—0
Joint translation and unit conversion for end-to-end localization—0
Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity—0
Just Ask:An Interactive Learning Framework for Vision and Language Navigation—0
Just-in-Time Detection of Silent Security Patches—0
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation—0
KaliCalib: A Framework for Basketball Court Registration—0
KeepOriginalAugment: Single Image-based Better Information-Preserving Data Augmentation Approach—0
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation—0
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT—0
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples—0
Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer—0
Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking—0
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP—0
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision—0
Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning—0
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning—0
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks—0
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions—0
KNU-HYUNDAI's NMT system for Scientific Paper and Patent Tasks onWAT 2019—0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator—0
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement—0
KU AIGEN ICL EDI@BC8 Track 3: Advancing Phenotype Named Entity Recognition and Normalization for Dysmorphology Physical Examination Reports—0
KUL@SMM4H’22: Template Augmented Adaptive Pre-training for Tweet Classification—0
Label Anchored Contrastive Learning for Language Understanding—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
Label-efficient audio classification through multitask learning and self-supervision—0
Label-Efficient Data Augmentation with Video Diffusion Models for Guidewire Segmentation in Cardiac Fluoroscopy—0
Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning—0
Label Geometry Aware Discriminator for Conditional Generative Networks—0
Label-guided Data Augmentation for Prompt-based Few Shot Learners—0
Label-Occurrence-Balanced Mixup for Long-tailed Recognition—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified