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 1851–1900 of 8378 papers

TitleStatusHype
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
Creation of Novel Soft Robot Designs using Generative AI—0
Credit Risk Identification in Supply Chains Using Generative Adversarial Networks—0
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Decoding Part-of-Speech from Human EEG Signals—0
Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
CrossCount: A Deep Learning System for Device-free Human Counting using WiFi—0
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC—0
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation—0
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection—0
Convolutional Neural Networks for Font Classification—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Convolutional Neural Networks for Automatic Meter Reading—0
A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation—0
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation—0
A Theory of PAC Learnability under Transformation Invariances—0
AugLoss: A Robust Augmentation-based Fine Tuning Methodology—0
A Target-Aware Analysis of Data Augmentation for Hate Speech Detection—0
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling—0
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil—0
Cross-lingual Inflection as a Data Augmentation Method for Parsing—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization—0
Cross-Modal Generative Augmentation for Visual Question Answering—0
Cross-Modality 3D Object Detection—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—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
Adaptive Regularization of Labels—0
A Causal View on Robustness of Neural Networks—0
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation—0
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—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