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 3751–3800 of 8378 papers

TitleStatusHype
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
A Competitive Method to VIPriors Object Detection Challenge—0
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
Graph Mixup with Soft Alignments—0
Diffusion Models for Robotic Manipulation: A Survey—0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
Impact of Aliasing on Generalization in Deep Convolutional Networks—0
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation—0
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection—0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization—0
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts—0
Diffusion Bridge Models for 3D Medical Image Translation—0
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation—0
AugLoss: A Robust Augmentation-based Fine Tuning Methodology—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation—0
Impact of Data Augmentation on QCNNs—0
Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses—0
Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images—0
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data—0
Diffusion-based Data Augmentation for Object Counting Problems—0
Boosting Cardiac Color Doppler Frame Rates with Deep Learning—0
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation—0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks—0
GUESS: Generative Uncertainty Ensemble for Self Supervision—0
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition—0
Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
An improved helmet detection method for YOLOv3 on an unbalanced dataset—0
Guided Discrete Diffusion for Electronic Health Record Generation—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets—0
Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells—0
Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment—0
HABD: a houma alliance book ancient handwritten character recognition database—0
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models—0
Hallucinations in neural machine translation—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Boosting Adversarial Transferability with Spatial Adversarial Alignment—0
HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models—0
Hand gesture recognition using 802.11ad mmWave sensor in the mobile device—0
DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition—0
An improved EfficientNetV2 for garbage 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