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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6951–6975 of 8378 papers

TitleStatusHype
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue—0
Effective Data Augmentation for Sentence Classification Using One VAE per Class—0
Effective Data Augmentation with Multi-Domain Learning GANs—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Effective face landmark localization via single deep network—0
Effective LLM Knowledge Learning via Model Generalization—0
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings—0
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data—0
Effect of GAN augmented dataset size on deep learning-based ultrasound bone segmentation model training—0
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning—0
Effects of Using Synthetic Data on Deep Recommender Models' Performance—0
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
Efficient Augmentation via Data Subsampling—0
Efficient Classification of Histopathology Images—0
Efficient data augmentation using graph imputation neural networks—0
Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging—0
Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach—0
Efficient, Lexicon-Free OCR using Deep Learning—0
Efficiently Trained Low-Resource Mongolian Text-to-Speech System Based On FullConv-TTS—0
Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection—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