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 5251–5300 of 8378 papers

TitleStatusHype
Accelerating Real-Time Question Answering via Question Generation—0
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation—0
Accessibility Considerations in the Development of an AI Action Plan—0
Accounting for Variance in Machine Learning Benchmarks—0
Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data—0
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets—0
Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning—0
Accurate Face Detection for High Performance—0
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG—0
Accurate synthesis of Dysarthric Speech for ASR data augmentation—0
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured—0
AclNet: efficient end-to-end audio classification CNN—0
A Close Look at Deep Learning with Small Data—0
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
A CNN-based methodology for breast cancer diagnosis using thermal images—0
A Coarse-to-Fine Auto-Sampler For Long-tailed Image Recognition—0
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition—0
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models—0
A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation—0
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition—0
A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers—0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit—0
A Comparison of Strategies for Source-Free Domain Adaptation—0
A comparison of streaming models and data augmentation methods for robust speech recognition—0
A Competitive Method to VIPriors Object Detection Challenge—0
A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques—0
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks—0
A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs—0
A Comprehensive Survey of Grammar Error Correction—0
A Comprehensive Survey on Data Augmentation—0
A Compromise Principle in Deep Monocular Depth Estimation—0
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images—0
A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests—0
A Continuous Mapping For Augmentation Design—0
ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models—0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech—0
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications—0
Active Generation Network of Human Skeleton for Action Recognition—0
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection—0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—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