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 3651–3700 of 8378 papers

TitleStatusHype
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks—0
RoentGen: Vision-Language Foundation Model for Chest X-ray GenerationCode1
Mitigating and Evaluating Static Bias of Action Representations in the Background and the ForegroundCode1
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive LearningCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence ModelingCode1
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition—0
Fed-TDA: Federated Tabular Data Augmentation on Non-IID DataCode1
Transformation-Equivariant 3D Object Detection for Autonomous Driving—0
Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted DataCode1
ModelDiff: A Framework for Comparing Learning AlgorithmsCode1
Improving Crowded Object Detection via Copy-Paste—0
EVNet: An Explainable Deep Network for Dimension ReductionCode1
RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural NetworkCode1
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models—0
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing DeepfakesCode1
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Background-Mixed Augmentation for Weakly Supervised Change DetectionCode1
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Coarse-to-fine Task-driven Inpainting for Geoscience Images—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Deep Learning on a Healthy Data Diet: Finding Important Examples for FairnessCode0
Quantifying Human Bias and Knowledge to guide ML models during Training—0
On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process—0
Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets—0
Simple and Effective Augmentation Methods for CSI Based Indoor Localization—0
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning—0
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering—0
3d human motion generation from the text via gesture action classification and the autoregressive model—0
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation—0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual BackbonesCode2
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation—0
Learning unfolded networks with a cyclic group structureCode0
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion RecognitionCode1
TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task—0
Semantic keypoint extraction for scanned animals using multi-depth-camera systemsCode0
Consecutive Question Generation via Dynamic Multitask Learning—0
Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network—0
Persian Emotion Detection using ParsBERT and Imbalanced Data Handling ApproachesCode0
Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation—0
Local Magnification for Data and Feature Augmentation—0
CardiacGen: A Hierarchical Deep Generative Model for Cardiac SignalsCode0
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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