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 4101–4150 of 8378 papers

TitleStatusHype
An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge—0
Bioacoustic Event Detection with prototypical networks and data augmentation—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation—0
Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to Advance ML-based Clinical Decision Support Systems for Early Prediction of Dialysis Among CKD Patients—0
Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets—0
Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion—0
Binary AddiVortes: (Bayesian) Additive Voronoi Tessellations for Binary Classification with an application to Predicting Home Mortgage Application Outcomes—0
A Comparison of Strategies for Source-Free Domain Adaptation—0
Detecting ESG topics using domain-specific language models and data augmentation approaches—0
Bi-modality Images Transfer with a Discrete Process Matching Method—0
An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains—0
Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques—0
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation—0
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception—0
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR—0
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions—0
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
A New Tool for Efficiently Generating Quality Estimation Datasets—0
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology—0
Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks—0
Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis—0
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting—0
A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human Pose Estimation—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
Bias Challenges in Counterfactual Data Augmentation—0
Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling—0
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems—0
Depression detection in social media posts using transformer-based models and auxiliary features—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks—0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
Dependent Relational Gamma Process Models for Longitudinal Networks—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions—0
BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages—0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit—0
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Dense Contrastive Visual-Linguistic Pretraining—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