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 3851–3900 of 8378 papers

TitleStatusHype
A Car Model Identification System for Streamlining the Automobile Sales Process—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
Hierarchical Scene Coordinate Classification and Regression for Visual Localization—0
FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation—0
FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection—0
Hierarchical Neural Data Synthesis for Semantic Parsing—0
A Survey on Data Augmentation for Text Classification—0
Hierarchical Topic Presence Models—0
FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals—0
Framework for lung CT image segmentation based on UNet++—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
Frame-level SpecAugment for Deep Convolutional Neural Networks in Hybrid ASR Systems—0
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models—0
Fractal interpolation in the context of prediction accuracy optimization—0
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction—0
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection—0
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection—0
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes—0
Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms—0
HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models—0
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells—0
A Grey-box Text Attack Framework using Explainable AI—0
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration—0
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach—0
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages—0
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks—0
Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems—0
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation—0
FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring—0
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation—0
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
A Survey of Uncertainty in Deep Neural Networks—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
How Does Mixup Help With Robustness and Generalization?—0
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
A survey of synthetic data augmentation methods in computer vision—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Foliar Uptake of Biocides: Statistical Assessment of Compartmental and Diffusion-Based Models—0
Focusing Image Generation to Mitigate Spurious Correlations—0
Contextual Scene Augmentation and Synthesis via GSACNet—0
How many labeled license plates are needed?—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
A Graph Data Augmentation Strategy with Entropy Preservation—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