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 5701–5750 of 8378 papers

TitleStatusHype
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
Neural Photometry-guided Visual Attribute Transfer—0
A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems—0
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather—0
Training Structured Neural Networks Through Manifold Identification and Variance ReductionCode0
Extracting knowledge from features with multilevel abstraction—0
Hierarchical Neural Data Synthesis for Semantic Parsing—0
Learning to Detect Every Thing in an Open World—0
The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge—0
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis—0
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors—0
Adaptive Data Augmentation on Temporal Graphs—0
EduMT: Developing Machine Translation System for Educational Content in Indian Languages—0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines—0
Explanation-based Data Augmentation for Image ClassificationCode0
A Continuous Mapping For Augmentation Design—0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain AugmentationCode0
Pattern-Aware Data Augmentation for LiDAR 3D Object Detection—0
Predicting Poverty Level from Satellite Imagery using Deep Neural Networks—0
Minor changes make a difference: a case study on the consistency of UD-based dependency parsersCode0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
Pyramid Adversarial Training Improves ViT PerformanceCode0
SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting—0
Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question MatchingCode0
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion—0
Do Invariances in Deep Neural Networks Align with Human Perception?Code0
Data Augmentation For Medical MR Image Using Generative Adversarial Networks—0
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images—0
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices—0
Data Augmented 3D Semantic Scene Completion with 2D Segmentation PriorsCode0
BCH-NLP at BioCreative VII Track 3: medications detection in tweets using transformer networks and multi-task learningCode0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Revisiting Contextual Toxicity Detection in Conversations—0
S-SimCSE: Sampled Sub-networks for Contrastive Learning of Sentence Embedding—0
Variance Reduction in Deep Learning: More Momentum is All You Need—0
Using mixup as regularization and tuning hyper-parameters for ResNetsCode0
Multi-Modality Microscopy Image Style Transfer for Nuclei Segmentation—0
Weight Pruning and Uncertainty in Radio Galaxy ClassificationCode0
Domain-Agnostic Clustering with Self-Distillation—0
Broad Adversarial Training with Data Augmentation in the Output Space—0
Video Content Swapping Using GAN—0
Simulated LiDAR Repositioning: a novel point cloud data augmentation method—0
Unsupervised Domain Adaptation for RF-based Gesture Recognition—0
An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood VesselsCode0
Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control—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