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.

Further readings:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1075 of 8378 papers

TitleStatusHype
Enhancing elusive clues in knowledge learning by contrasting attention of language modelsCode0
Improving satellite imagery segmentation using multiple Sentinel-2 revisitsCode0
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial NetworksCode0
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition—0
Small data deep learning methodology for in-field disease detection—0
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and VerificationCode0
Self-Supervised Any-Point Tracking by Contrastive Random WalksCode2
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based ModelsCode1
Vision-based Xylem Wetness Classification in Stem Water Potential Determination—0
Machine learning approaches for automatic defect detection in photovoltaic systemsCode0
Adversarial Backdoor Defense in CLIP—0
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AICode0
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks—0
Region Mixup—0
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading ComprehensionCode0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers—0
Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks—0
WeatherFormer: Empowering Global Numerical Weather Forecasting with Space-Time Transformer—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