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 1401–1450 of 8378 papers

TitleStatusHype
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis—0
Improving Grammatical Error Correction via Contextual Data AugmentationCode0
Generative Expansion of Small Datasets: An Expansive Graph Approach—0
Sound Tagging in Infant-centric Home Soundscapes—0
Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation—0
MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptionsCode1
Detection of Synthetic Face Images: Accuracy, Robustness, Generalization—0
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency—0
MixTex: Unambiguous Recognition Should Not Rely Solely on Real DataCode5
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Task Oriented In-Domain Data Augmentation—0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language UnderstandingCode0
Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting—0
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language ModelsCode1
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification—0
Self Training and Ensembling Frequency Dependent Networks with Coarse Prediction Pooling and Sound Event Bounding BoxesCode1
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
RuleR: Improving LLM Controllability by Rule-based Data RecyclingCode1
PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology ImagesCode0
Exploring Audio-Visual Information Fusion for Sound Event Localization and Detection In Low-Resource Realistic Scenarios—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Self-supervised Brain Lesion Generation for Effective Data Augmentation of Medical Images—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Voice Disorder Analysis: a Transformer-based ApproachCode1
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation—0
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Self-Supervised Pretext Tasks for Alzheimer's Disease Classification using 3D Convolutional Neural Networks on Large-Scale Synthetic Neuroimaging Dataset—0
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching—0
A New Approach for Evaluating and Improving the Performance of Segmentation Algorithms on Hard-to-Detect Blood VesselsCode0
Visually Robust Adversarial Imitation Learning from Videos with Contrastive LearningCode0
Skin Cancer Images Classification using Transfer Learning Techniques—0
Class-specific Data Augmentation for Plant Stress ClassificationCode0
Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition—0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling—0
Insect Identification in the Wild: The AMI DatasetCode0
Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Is Your HD Map Constructor Reliable under Sensor Corruptions?—0
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation—0
Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning—0
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical ReasoningCode2
Multispectral Snapshot Image Registration Using Learned Cross Spectral Disparity Estimation and a Deep Guided Occlusion Reconstruction NetworkCode0
Deep Learning methodology for the identification of wood species using high-resolution macroscopic imagesCode0
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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