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 3351–3375 of 8378 papers

TitleStatusHype
Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Curriculum Data Augmentation for Low-Resource Slides Summarization—0
Augmentation Policy Generation for Image Classification Using Large Language Models—0
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification—0
A data-centric approach to class-specific bias in image data augmentation—0
Accessibility Considerations in the Development of an AI Action Plan—0
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images—0
Label Augmentation for Neural Networks Robustness—0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems—0
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss—0
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
A low-cost PPG sensor-based empirical study on healthy aging based on changes in PPG morphology—0
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection—0
CST5: Data augmentation for Code-Switched Semantic Parsing—0
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages—0
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3—0
Augmentation Learning for Semi-Supervised Classification—0
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System—0
Augmentation Invariant Manifold Learning—0
CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training—0
Augmentation Inside the Network—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