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 3126–3150 of 8378 papers

TitleStatusHype
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
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis—0
Explainable Deep Learning for Augmentation of sRNA Expression Profiles—0
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification—0
Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation—0
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation—0
Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation—0
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task—0
AS-ES Learning: Towards Efficient CoT Learning in Small Models—0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring—0
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients—0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Explicit Modeling the Context for Chinese NER—0
Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation—0
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data—0
FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—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