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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4301–4350 of 8378 papers

TitleStatusHype
Delexicalized Paraphrase Generation—0
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC—0
Denoising Diffusion Medical Models—0
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers—0
Dense Contrastive Visual-Linguistic Pretraining—0
Dependent Relational Gamma Process Models for Longitudinal Networks—0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Depression detection in social media posts using transformer-based models and auxiliary features—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis—0
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions—0
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation—0
Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques—0
Detecting ESG topics using domain-specific language models and data augmentation approaches—0
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge—0
Detecting Prefix Bias in LLM-based Reward Models—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing—0
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention—0
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers—0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Detection of Synthetic Face Images: Accuracy, Robustness, Generalization—0
Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques—0
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation—0
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites—0
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks—0
Developing neural machine translation models for Hungarian-English—0
Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Diabetes detection using deep learning techniques with oversampling and feature augmentation—0
Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN—0
Diabetic retinopathy image classification method based on GreenBen data augmentation—0
Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method—0
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation—0
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
Optimized Deep Encoder-Decoder Methods for Crack Segmentation—0
Optimizing Alignment with Less: Leveraging Data Augmentation for Personalized Evaluation—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
Optimizing Encoder-Only Transformers for Session-Based Recommendation Systems—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