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 6651–6700 of 8378 papers

TitleStatusHype
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
FastIF: Scalable Influence Functions for Efficient Model Interpretation and DebuggingCode0
Neural Machine Translation: A Review of Methods, Resources, and Tools—0
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention—0
Data augmentation and image understanding—0
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)—0
Self-supervised Pre-training with Hard Examples Improves Visual Representations—0
Adversarial Momentum-Contrastive Pre-TrainingCode0
Speech Synthesis as Augmentation for Low-Resource ASR—0
Automated Lay Language Summarization of Biomedical Scientific ReviewsCode0
MetaAugment: Sample-Aware Data Augmentation Policy Learning—0
Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning—0
Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature—0
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems—0
ResizeMix: Mixing Data with Preserved Object Information and True Labels—0
Augmentation Inside the Network—0
Recent Advances of Generic Object Detection with Deep Learning: A Review—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Joint Search of Data Augmentation Policies and Network Architectures—0
Sparse Signal Models for Data Augmentation in Deep Learning ATRCode0
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting—0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks—0
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation—0
Biomechanical modelling of brain atrophy through deep learning—0
A Coarse-to-Fine Auto-Sampler For Long-tailed Image Recognition—0
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation—0
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data—0
Multilingual Transfer Learning for QA Using Translation as Data Augmentation—0
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection—0
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU—0
MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias—0
Generative Data Augmentation for Vehicle Detection in Aerial Images—0
Neural Rate Control for Video Encoding using Imitation Learning—0
VAE-Info-cGAN: Generating Synthetic Images by Combining Pixel-level and Feature-level Geospatial Conditional Inputs—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
Data InStance Prior (DISP) in Generative Adversarial Networks—0
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text—0
GenScan: A Generative Method for Populating Parametric 3D Scan Datasets—0
Frame-level SpecAugment for Deep Convolutional Neural Networks in Hybrid ASR Systems—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Enhanced Offensive Language Detection Through Data Augmentation—0
Generating Synthetic Multispectral Satellite Imagery from Sentinel-2—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Data-Efficient Methods for Dialogue Systems—0
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System—0
Kernel-convoluted Deep Neural Networks with Data AugmentationCode0
Boosting offline handwritten text recognition in historical documents with few labeled lines—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