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.

Further readings:

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Papers

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Showing 6801–6850 of 8378 papers

TitleStatusHype
Diffusion Model with Clustering-based Conditioning for Food Image Generation—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models—0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
Digital Operating Mode Classification of Real-World Amateur Radio Transmissions—0
Digital Signal Processing Using Deep Neural Networks—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
Disambiguated Lexically Constrained Neural Machine Translation—0
Disambiguation of morpho-syntactic features of African American English -- the case of habitual be—0
Disambiguation of morpho-syntactic features of African American English – the case of habitual be—0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Discrete Latent Perspective Learning for Segmentation and Detection—0
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—0
Discriminative Relational Topic Models—0
Discriminative Reranking for Neural Machine Translation—0
Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images—0
Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions—0
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine—0
Disease Severity Regression with Continuous Data Augmentation—0
Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization—0
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data—0
Distillation of Diffusion Features for Semantic Correspondence—0
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
Distilling Calibrated Student from an Uncalibrated Teacher—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Distilling Large Language Models into Tiny and Effective Students using pQRNN—0
Distilling Transformers for Neural Cross-Domain Search—0
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images—0
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling—0
Distributionally Robust Cross Subject EEG Decoding—0
Distribution augmentation for low-resource expressive text-to-speech—0
Diverse Ensembles Improve Calibration—0
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers—0
Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding—0
Diversity-Oriented Data Augmentation with Large Language Models—0
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning—0
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation—0
DM-CT: Consistency Training with Data and Model Perturbation—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