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 4051–4100 of 8378 papers

TitleStatusHype
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
Improved baselines for vision-language pre-training—0
An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network—0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing—0
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks—0
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework—0
Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques—0
Improved Data Augmentation for Translation Suggestion—0
Improved English to Hindi Multimodal Neural Machine Translation—0
Data augmentation for efficient learning from parametric experts—0
Indirect Gradient Matching for Adversarial Robust Distillation—0
Improved Image-based Pose Regressor Models for Underwater Environments—0
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites—0
Improved Meta-Learning Training for Speaker Verification—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation—0
Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques—0
Improved POS tagging for spontaneous, clinical speech using data augmentation—0
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks—0
Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control—0
Improved Recurrent Neural Networks for Session-based Recommendations—0
In Defense of Single-column Networks for Crowd Counting—0
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments—0
Improved Regularization Techniques for End-to-End Speech Recognition—0
Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation—0
Improved resistance of neural networks to adversarial images through generative pre-training—0
Detection of Synthetic Face Images: Accuracy, Robustness, Generalization—0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Biomechanical modelling of brain atrophy through deep learning—0
Improved Techniques For Weakly-Supervised Object Localization—0
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers—0
Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending—0
A Unified Framework for Generative Data Augmentation: A Comprehensive Survey—0
Improve Learning from Crowds via Generative Augmentation—0
Data Augmentation for Image Classification using Generative AI—0
Improving 3D Object Detection through Progressive Population Based Augmentation—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—0
Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models—0
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning—0
An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation—0
Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing—0
BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Detecting Prefix Bias in LLM-based Reward Models—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