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 7151–7175 of 8378 papers

TitleStatusHype
Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays—0
Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images—0
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models—0
Evaluation of generative networks through their data augmentation capacity—0
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies—0
EvAnimate: Event-conditioned Image-to-Video Generation for Human Animation—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning—0
Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras—0
EventMix: An Efficient Augmentation Strategy for Event-Based Data—0
EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision—0
Everything is Varied: The Surprising Impact of Individual Variation on ML Robustness in Medicine—0
Evolutionary Augmentation Policy Optimization for Self-supervised Learning—0
Evolution without Large Models: Training Language Model with Task Principles—0
Evolving Image Compositions for Feature Representation Learning—0
EvTTC: An Event Camera Dataset for Time-to-Collision Estimation—0
Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images—0
Examining the Effects of Language-and-Vision Data Augmentation for Generation of Descriptions of Human Faces—0
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Exoplanet Detection by Machine Learning with Data Augmentation—0
Generative Expansion of Small Datasets: An Expansive Graph Approach—0
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)—0
Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating—0
Experimenting with Large Language Models and vector embeddings in NASA SciX—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