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:

( Image credit: Albumentations )

Papers

Showing 48014850 of 8378 papers

TitleStatusHype
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes0
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement LearningCode0
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning0
DATScore: Evaluating Translation with Data Augmented Translations0
Improved Data Augmentation for Translation Suggestion0
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement0
A multi-category inverse design neural network and its application to diblock copolymers0
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data AugmentationCode0
Improving Sample Efficiency of Deep Learning Models in Electricity Market0
Checks and Strategies for Enabling Code-Switched Machine Translation0
T5 for Hate Speech, Augmented Data and EnsembleCode0
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?Code0
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech0
Domain-guided data augmentation for deep learning on medical imaging0
Everything is Varied: The Surprising Impact of Individual Variation on ML Robustness in Medicine0
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing0
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive PretrainingCode0
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspectiveCode0
CORE: A Retrieve-then-Edit Framework for Counterfactual Data GenerationCode0
Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts0
VM-NeRF: Tackling Sparsity in NeRF with View MorphingCode0
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation ModelsCode0
Galaxy Spin Classification I: Z-wise vs S-wise Spirals With Chirality Equivariant Residual NetworkCode0
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction0
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning0
Evaluating the Performance of StyleGAN2-ADA on Medical Images0
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?0
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentationCode0
Data-driven Approaches to Surrogate Machine Learning Model Development0
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus ImagesCode0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks0
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene0
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift0
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation0
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation0
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems0
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical DatasetsCode0
MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations0
Smooth image-to-image translations with latent space interpolationsCode0
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation0
GraDA: Graph Generative Data Augmentation for Commonsense ReasoningCode0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)0
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective0
Lightweight Contextual Logical Structure Recovery0
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding0
Automated segmentation of microvessels in intravascular OCT images using deep learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6Unverified
5ResNet-200 (UA)Accuracy (%)80.4Unverified
6ResNet-200 (AA)Accuracy (%)80Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7Unverified
2PaCMAPClassification Accuracy85.3Unverified
3hNNEClassification Accuracy77.4Unverified
4TopoAEClassification Accuracy74.6Unverified