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 551–600 of 8378 papers

TitleStatusHype
A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet DatasetCode1
A U-Net Based Discriminator for Generative Adversarial NetworksCode1
DALE: Generative Data Augmentation for Low-Resource Legal NLPCode1
Data Augmentation for Deep Candlestick LearnerCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Data Optimization in Deep Learning: A SurveyCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
CutDepth:Edge-aware Data Augmentation in Depth EstimationCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram SynthesisCode1
An Effective and Robust Detector for Logo DetectionCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
CultureLLM: Incorporating Cultural Differences into Large Language ModelsCode1
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
Aspect-Controlled Neural Argument GenerationCode1
A disentangled generative model for disease decomposition in chest X-rays via normal image synthesisCode1
Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text GuidanceCode1
Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning MethodCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimesCode1
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
Anchor-free Small-scale Multispectral Pedestrian DetectionCode1
Cross-modality Data Augmentation for End-to-End Sign Language TranslationCode1
Cross-modulated Few-shot Image Generation for Colorectal Tissue ClassificationCode1
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
CST5: Data Augmentation for Code-Switched Semantic ParsingCode1
3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian LocalizationCode1
A Survey on Recent Approaches for Natural Language Processing in Low-Resource ScenariosCode1
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram DigitizationCode1
Unified Domain Adaptive Semantic SegmentationCode1
A Simple Semi-Supervised Learning Framework for Object DetectionCode1
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical ReasoningCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
An Empirical Study of CLIP for Text-based Person SearchCode1
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and BeyondCode1
Data Augmentation for Deep Graph Learning: A SurveyCode1
An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure SystemsCode1
AugCSE: Contrastive Sentence Embedding with Diverse AugmentationsCode1
Data Augmentation for ElectrocardiogramsCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support ConversationCode1
An Empirical Survey of Data Augmentation for Time Series Classification with Neural NetworksCode1
A Diffusion Model Predicts 3D Shapes from 2D Microscopy ImagesCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
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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