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 1301–1350 of 8378 papers

TitleStatusHype
Seismic Fault SAM: Adapting SAM with Lightweight Modules and 2.5D Strategy for Fault Detection—0
Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning-Based Audio Enhancement: Algorithm Development and Validation—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments—0
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIsCode0
Struct-X: Enhancing Large Language Models Reasoning with Structured Data—0
Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection—0
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action RecognitionCode1
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic DataCode2
Team HYU ASML ROBOVOX SP Cup 2024 System Description—0
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 ChallengeCode1
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGenCode1
XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI ApproachCode0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
CIC-BART-SSA: Controllable Image Captioning with Structured Semantic AugmentationCode0
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition NetworksCode0
MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation—0
Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?—0
GeoMix: Towards Geometry-Aware Data AugmentationCode0
TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction—0
Melon Fruit Detection and Quality Assessment Using Generative AI-Based Image Data Augmentation—0
An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robotsCode0
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data—0
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
Semi-supervised 3D Object Detection with PatchTeacher and PillarMixCode0
Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability FieldsCode0
Minimizing PLM-Based Few-Shot Intent DetectorsCode0
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control—0
Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers—0
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering—0
Bora: Biomedical Generalist Video Generation Model—0
CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting—0
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer—0
Investigating Public Fine-Tuning Datasets: A Complex Review of Current Practices from a Construction Perspective—0
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model—0
Enrich the content of the image Using Context-Aware Copy Paste—0
An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio—0
DALL-M: Context-Aware Clinical Data Augmentation with LLMsCode0
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization—0
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches—0
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
SUMix: Mixup with Semantic and Uncertain InformationCode1
Video-to-Audio Generation with Hidden Alignment—0
VEnhancer: Generative Space-Time Enhancement for Video Generation—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Sustainable techniques to improve Data Quality for training image-based explanatory models for Recommender Systems—0
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics—0
Assessing Cardiomegaly in Dogs Using a Simple CNN Model—0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
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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