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 5501–5550 of 8378 papers

TitleStatusHype
A Meta Understanding of Meta-Learning—0
A Method of Data Augmentation to Train a Small Area Fingerprint Recognition Deep Neural Network with a Normal Fingerprint Database—0
A Mobile Food Recognition System for Dietary Assessment—0
A Model Generalization Study in Localizing Indoor Cows with COw LOcalization (COLO) dataset—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages—0
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection—0
A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts—0
A multi-category inverse design neural network and its application to diblock copolymers—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
A Multimodal Approach for Advanced Pest Detection and Classification—0
A Multi-Scale Conditional Deep Model for Tumor Cell Ratio Counting—0
A multiscale spatiotemporal approach for smallholder irrigation detection—0
A multi-source approach for Breton–French hybrid machine translation—0
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation—0
A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance—0
A Multi-view Perspective of Self-supervised Learning—0
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation—0
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances—0
An Advanced NLP Framework for Automated Medical Diagnosis with DeBERTa and Dynamic Contextual Positional Gating—0
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design—0
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection—0
Analisis Eksploratif Dan Augmentasi Data NSL-KDD Menggunakan Deep Generative Adversarial Networks Untuk Meningkatkan Performa Algoritma Extreme Gradient Boosting Dalam Klasifikasi Jenis Serangan Siber—0
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment—0
Analysis of Convolutional Decoder for Image Caption Generation—0
Analysis of Convolutional Neural Networks for Document Image Classification—0
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Analytical Moment Regularizer for Training Robust Networks—0
Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset—0
Analyzing ASR pretraining for low-resource speech-to-text translation—0
Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability—0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks—0
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding—0
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification—0
An Approach to Improve Robustness of NLP Systems against ASR Errors—0
Anatomical Data Augmentation For CNN based Pixel-wise Classification—0
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning—0
AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation—0
Anatomy-specific classification of medical images using deep convolutional nets—0
An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation—0
An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging—0
An Augmentation Strategy for Visually Rich Documents—0
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding—0
An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction—0
Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation—0
An Effective Crop-Paste Pipeline for Few-shot Object Detection—0
An Effective Hit-or-Miss Layer Favoring Feature Interpretation as Learned Prototypes Deformations—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