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 8201–8250 of 8378 papers

TitleStatusHype
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfer—0
Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation—0
Leveraging the Power of Data Augmentation for Transformer-based Tracking—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant—0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge—0
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection—0
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Lie Point Symmetry and Physics Informed Networks—0
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers—0
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection—0
Light Weight CNN for classification of Brain Tumors from MRI Images—0
Lightweight Contextual Logical Structure Recovery—0
Lightweight Convolutional Neural Networks for Retinal Disease Classification—0
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data—0
Lightweight, Uncertainty-Aware Conformalized Visual Odometry—0
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features—0
Limitations of Data-Driven Spectral Reconstruction -- Optics-Aware Analysis and Mitigation—0
Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection—0
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation—0
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing—0
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing—0
Linearly Convergent Mixup Learning—0
LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction—0
Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation—0
LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect—0
LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context Disambiguation—0
LIP: Learning Instance Propagation for Video Object Segmentation—0
Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells—0
LLaVA-Zip: Adaptive Visual Token Compression with Intrinsic Image Information—0
LLM-based phoneme-to-grapheme for phoneme-based speech recognition—0
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification—0
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition—0
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching—0
LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods—0
LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation—0
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?—0
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition—0
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition—0
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI—0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs—0
Localization of Malaria Parasites and White Blood Cells in Thick Blood Smears—0
Localized Contrastive Learning on Graphs—0
Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques—0
Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting—0
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks—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