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 5601–5650 of 8378 papers

TitleStatusHype
Fair Node Representation Learning via Adaptive Data Augmentation—0
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze EstimationCode0
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction—0
Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest RadiographsCode0
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation—0
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning—0
Syntax-based data augmentation for Hungarian-English machine translationCode0
MODALS: Data augmentation that works for everyone—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
Label-guided Data Augmentation for Prompt-based Few Shot Learners—0
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework—0
SUBS: Subtree Substitution for Compositional Semantic Parsing—0
Improving negation detection with negation-focused pre-training—0
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning—0
Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices—0
Sentence-Level Resampling for Named Entity Recognition—0
Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation—0
Data Augmentation for Low-Resource Dialogue Summarization—0
Improving Robustness in Multilingual Machine Translation via Data Augmentation—0
Data Augmentation for Biomedical Factoid Question Answering—0
Recent Progress in the CUHK Dysarthric Speech Recognition System—0
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition—0
Investigation of Data Augmentation Techniques for Disordered Speech Recognition—0
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization—0
Multi-task Pre-training Language Model for Semantic Network CompletionCode0
Making a (Counterfactual) Difference One Rationale at a TimeCode0
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting—0
Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial NetworksCode0
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images—0
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks—0
Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
A study on cross-corpus speech emotion recognition and data augmentation—0
Invariance encoding in sliced-Wasserstein space for image classification with limited training dataCode0
GenLabel: Mixup Relabeling using Generative Models—0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition—0
Semantic-based Data Augmentation for Math Word Problems—0
Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection—0
Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy ClassificationCode0
Learning to Generate Novel Classes for Deep Metric Learning—0
FROTE: Feedback Rule-Driven Oversampling for Editing Models—0
Data Augmentation for Depression Detection Using Skeleton-Based Gait Information—0
Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models—0
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection—0
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection—0
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud—0
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection—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