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 6101–6150 of 8378 papers

TitleStatusHype
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer LabelsCode1
Modeling tail risks of inflation using unobserved component quantile regressionsCode1
VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning ChallengesCode1
FloMo: Tractable Motion Prediction with Normalizing FlowsCode0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration—0
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision—0
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks—0
Data Augmentation for Object Detection via Differentiable Neural RenderingCode1
Domain Generalization: A Survey—0
Multi-attentional Deepfake DetectionCode1
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing—0
On the effectiveness of adversarial training against common corruptionsCode1
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis—0
Pseudo-labeling for Scalable 3D Object Detection—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine LearningCode0
Diffusion Probabilistic Models for 3D Point Cloud GenerationCode1
Context Decoupling Augmentation for Weakly Supervised Semantic SegmentationCode1
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
Fixing Data Augmentation to Improve Adversarial RobustnessCode1
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound ClassificationCode1
Accounting for Variance in Machine Learning Benchmarks—0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
On the Fairness of Generative Adversarial Networks (GANs)—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket PerspectiveCode1
Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads—0
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data AugmentationCode0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical ImagingCode1
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images—0
Learning with invariances in random features and kernel models—0
Data-Driven Incident Detection in Power Distribution Systems—0
Understanding Robustness in Teacher-Student Setting: A New Perspective—0
Robust Pollen Imagery Classification with Generative Modeling and Mixup Training—0
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition—0
An Enhanced Prohibited Items Recognition Model—0
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing—0
Robust SleepNetsCode0
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant FeaturesCode1
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture—0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model—0
Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation—0
Analyzing Overfitting under Class Imbalance in Neural Networks for Image SegmentationCode1
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-LearningCode1
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—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