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 2151–2200 of 8378 papers

TitleStatusHype
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
A study on cross-corpus speech emotion recognition and data augmentation—0
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
A Generative Neural Annealer for Black-Box Combinatorial Optimization—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
A Study of Transfer Learning in Music Source Separation—0
Conditional Synthetic Food Image Generation—0
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data—0
A study of the impact of generative AI-based data augmentation on software metadata classification—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
Conditional set generation using Seq2seq models—0
Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition—0
Adaptive Data Augmentation for Thompson Sampling—0
Abstract Text Summarization: A Low Resource Challenge—0
Conditional Generative Data Augmentation for Clinical Audio Datasets—0
Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery—0
A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning—0
Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
A Study of Augmentation Methods for Handwritten Stenography Recognition—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Conditional Augmentation for Generative Modeling—0
Adaptive Data Augmentation for Contrastive Learning—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Conditional Adversarial Synthesis of 3D Facial Action Units—0
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty—0
Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network—0
Concurrent Adversarial Learning for Large-Batch Training—0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
A general framework for defining and optimizing robustness—0
CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech—0
Learning Visual Representations with Optimum-Path Forest and its Applications to Barrett's Esophagus and Adenocarcinoma Diagnosis—0
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models—0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
Computational Ceramicology—0
Assessment Framework for Deepfake Detection in Real-world Situations—0
Computational Approaches to Arabic-English Code-Switching—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified