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 7851–7900 of 8378 papers

TitleStatusHype
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data—0
Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs—0
Improving robustness against common corruptions with frequency biased models—0
Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning-Based Audio Enhancement: Algorithm Development and Validation—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Improving Robustness in Multilingual Machine Translation via Data Augmentation—0
Improving Robustness of Language Models from a Geometry-aware Perspective—0
Improving robustness of language models from a geometry-aware perspective—0
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images—0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method—0
Improving Robustness of Task Oriented Dialog Systems—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation—0
Improving Robustness with Image Filtering—0
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor—0
Improving Sample Efficiency of Deep Learning Models in Electricity Market—0
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning—0
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation—0
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition—0
Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering—0
Improving speaker verification robustness with synthetic emotional utterances—0
Improving Spoken Language Understanding by Wisdom of Crowds—0
Improving Temporal Relation Extraction with Training Instance Augmentation—0
Improving Text Relationship Modeling with Artificial Data—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Improving the Effectiveness of Deep Generative Data—0
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation—0
Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing—0
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals—0
Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation—0
Improving the performance of weak supervision searches using data augmentation—0
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input—0
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning—0
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement—0
Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networks—0
Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation—0
Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation—0
Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)—0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation—0
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching—0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images—0
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images—0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation—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