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 6201–6250 of 8378 papers

TitleStatusHype
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images—0
Combining Ensembles and Data Augmentation can Harm your Calibration—0
Combining Euclidean Alignment and Data Augmentation for BCI decoding—0
Combining High-Level Features of Raw Audio Waves and Mel-Spectrograms for Audio Tagging—0
Combining Image Features and Patient Metadata to Enhance Transfer Learning—0
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion—0
Combining Transformer Generators with Convolutional Discriminators—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine—0
ComOM at VLSP 2023: A Dual-Stage Framework with BERTology and Unified Multi-Task Instruction Tuning Model for Vietnamese Comparative Opinion Mining—0
Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification—0
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation—0
Comparative Analysis of Extrinsic Factors for NER in French—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Comparative Analysis of Mel-Frequency Cepstral Coefficients and Wavelet Based Audio Signal Processing for Emotion Detection and Mental Health Assessment in Spoken Speech—0
Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning—0
Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems—0
Comparing Methods for Bias Mitigation in Graph Neural Networks—0
Comparing MT Approaches for Text Normalization—0
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors—0
Complementary Systems for Off-Topic Spoken Response Detection—0
ComplexFace: a Multi-Representation Approach for Image Classification with Small Dataset—0
Complex Wavelet SSIM based Image Data Augmentation—0
Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition—0
Compositional Attribute Imbalance in Vision Datasets—0
Compositional Data Augmentation for Abstractive Conversation Summarization—0
Compositional Generalization for Kinship Prediction through Data Augmentation—0
Compositional pre-training for neural semantic parsing—0
Compositional Zero-Shot Domain Transfer with Text-to-Text Models—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
Computational Approaches to Arabic-English Code-Switching—0
Computational Ceramicology—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech—0
Concurrent Adversarial Learning for Large-Batch Training—0
Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network—0
Conditional Adversarial Synthesis of 3D Facial Action Units—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Conditional Augmentation for Generative Modeling—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs—0
Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery—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