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 6526–6550 of 8378 papers

TitleStatusHype
Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads—0
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data AugmentationCode0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images—0
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition—0
Data-Driven Incident Detection in Power Distribution Systems—0
Understanding Robustness in Teacher-Student Setting: A New Perspective—0
Learning with invariances in random features and kernel models—0
Robust Pollen Imagery Classification with Generative Modeling and Mixup Training—0
An Enhanced Prohibited Items Recognition Model—0
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing—0
Robust SleepNetsCode0
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture—0
Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation—0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Semi-Supervised Singing Voice Separation with Noisy Self-Training—0
QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time—0
Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification—0
When and How Mixup Improves Calibration—0
Auctus: A Dataset Search Engine for Data Augmentation—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