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 6351–6400 of 8378 papers

TitleStatusHype
Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions—0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis—0
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
Creation of Novel Soft Robot Designs using Generative AI—0
Credit Risk Identification in Supply Chains Using Generative Adversarial Networks—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization—0
Cross-Corpus Data Augmentation for Acoustic Addressee Detection—0
CrossCount: A Deep Learning System for Device-free Human Counting using WiFi—0
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
Cross-language Sentence Selection via Data Augmentation and Rationale Training—0
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems—0
Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages—0
Cross-lingual Inflection as a Data Augmentation Method for Parsing—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization—0
Cross-Modal Generative Augmentation for Visual Question Answering—0
Cross-Modality 3D Object Detection—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment—0
Cross-regularization: Adaptive Model Complexity through Validation Gradients—0
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation—0
Cross-speaker style transfer for text-to-speech using data augmentation—0
CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training—0
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System—0
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3—0
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages—0
CST5: Data augmentation for Code-Switched Semantic Parsing—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss—0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Curriculum Data Augmentation for Low-Resource Slides Summarization—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Curriculum-style Data Augmentation for LLM-based Metaphor Detection—0
Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion—0
Cut-and-Paste with Precision: a Content and Perspective-aware Data Augmentation for Road Damage Detection—0
Cut out the annotator, keep the cutout: better segmentation with weak supervision—0
Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models—0
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity—0
Cutting-Splicing data augmentation: A novel technology for medical image segmentation—0
CVAE-based Re-anchoring for Implicit Discourse Relation Classification—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