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 1551–1600 of 8378 papers

TitleStatusHype
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection—0
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks—0
A Recipe for Unbounded Data Augmentation in Visual Reinforcement LearningCode1
DualContrast: Unsupervised Disentangling of Content and Transformations with Implicit Parameterization—0
Saturn: Sample-efficient Generative Molecular Design using Memory ManipulationCode2
GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement LearningCode1
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction—0
TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time SeriesCode1
Planted: a dataset for planted forest identification from multi-satellite time series—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
Combining Euclidean Alignment and Data Augmentation for BCI decoding—0
Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
Can LLMs Solve longer Math Word Problems Better?Code0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Data Augmentation Techniques for Process Extraction from Scientific Publications—0
Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure—0
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation—0
YOLOv10: Real-Time End-to-End Object DetectionCode11
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images—0
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood DiscrepancyCode1
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction TuningCode1
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification—0
Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G NetworksCode1
Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
Generalize Polyp Segmentation via Inpainting across Diverse Backgrounds and Pseudo-Mask RefinementCode0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Data Augmentation for Text-based Person Retrieval Using Large Language Models—0
Speech-dependent Data Augmentation for Own Voice Reconstruction with Hearable Microphones in Noisy Environments—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge SystemCode4
Team Samsung-RAL: Technical Report for 2024 RoboDrive Challenge-Robust Map Segmentation Track—0
MixCut:A Data Augmentation Method for Facial Expression Recognition—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Simultaneous Masking, Not Prompting Optimization: A Paradigm Shift in Fine-tuning LLMs for Simultaneous TranslationCode1
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
Training Deep Learning Models with Hybrid Datasets for Robust Automatic Target Detection on real SAR images—0
A Comprehensive Survey on Data Augmentation—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