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 3551–3600 of 8378 papers

TitleStatusHype
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation—0
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
General GAN-generated image detection by data augmentation in fingerprint domain—0
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation—0
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search—0
Discrete Latent Perspective Learning for Segmentation and Detection—0
Generalizability through Explainability: Countering Overfitting with Counterfactual Examples—0
Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation—0
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings—0
Generalization and Stability of GANs: A theory and promise from data augmentation—0
Generalization bounds via distillation—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning—0
Hide and Seek: How Does Watermarking Impact Face Recognition?—0
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization—0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan—0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Generalization to translation shifts: a study in architectures and augmentations—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generalized Data Augmentation for Low-Resource Translation—0
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory—0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Convolutional Neural Networks for Automatic Meter Reading—0
Generalizing Back-Translation in Neural Machine Translation—0
Convolutional Neural Networks for Font Classification—0
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning—0
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks—0
Disambiguation of morpho-syntactic features of African American English – the case of habitual be—0
An object-centric sensitivity analysis of deep learning based instance segmentation—0
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations—0
Disambiguation of morpho-syntactic features of African American English -- the case of habitual be—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
Disambiguated Lexically Constrained Neural Machine Translation—0
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Boosting Neural Machine Translation with Similar Translations—0
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text—0
Generating artificial texts as substitution or complement of training data—0
Generating Data Augmentation samples for Semantic Segmentation of Salt Bodies in a Synthetic Seismic Image Dataset—0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior—0
Generating Diverse Translation by Manipulating Multi-Head Attention—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Deep Neural Network Augmentation: Generating Faces for Affect Analysis—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement—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