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 1951–2000 of 8378 papers

TitleStatusHype
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models—0
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase—0
Controllable Text Simplification with Explicit Paraphrasing—0
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy—0
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection—0
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies—0
DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages—0
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm—0
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions—0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback—0
Co(ve)rtex: ML Models as storage channels and their (mis-)applications—0
Accounting for Variance in Machine Learning Benchmarks—0
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models—0
Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming—0
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation—0
A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction—0
Data Augmentation and Clustering for Vehicle Make/Model Classification—0
Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset—0
Data augmentation and explainability for bias discovery and mitigation in deep learning—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Data augmentation and image understanding—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
Adaptive Neural Networks for Intelligent Data-Driven Development—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation—0
Data Augmentation and Squeeze-and-Excitation Network on Multiple Dimension for Sound Event Localization and Detection in Real Scenes—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Data augmentation approaches for improving animal audio classification—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
Contrastive Visual Data Augmentation—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
How Data Augmentation affects Optimization for Linear Regression—0
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization—0
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions—0
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking—0
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
Deep neural network ensemble by data augmentation and bagging for skin lesion 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