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 2501–2550 of 8378 papers

TitleStatusHype
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
T-GAE: Transferable Graph Autoencoder for Network AlignmentCode0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
How Good Are Synthetic Medical Images? An Empirical Study with Lung UltrasoundCode0
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation—0
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation—0
A Recipe for Improved Certifiable RobustnessCode1
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts—0
Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection—0
Randomized Dimension Reduction with Statistical Guarantees—0
Learnable Data Augmentation for One-Shot Unsupervised Domain AdaptationCode0
Understanding Masked Autoencoders From a Local Contrastive Perspective—0
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?—0
Fetal-BET: Brain Extraction Tool for Fetal MRICode0
Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models—0
It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera—0
Incorporating Supervised Domain Generalization into Data Augmentation—0
Target-Aware Contextual Political Bias Detection in News—0
Robust Sentiment Analysis for Low Resource languages Using Data Augmentation Approaches: A Case Study in Marathi—0
Understanding Robust Overfitting from the Feature Generalization Perspective—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Pixel-Inconsistency Modeling for Image Manipulation Localization—0
Structural Adversarial Objectives for Self-Supervised Representation LearningCode0
A Unified Framework for Generative Data Augmentation: A Comprehensive Survey—0
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback—0
Asynchronous Graph GeneratorCode0
On the Equivalence of Graph Convolution and MixupCode0
Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect SegmentationCode0
SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion ModelsCode0
Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems—0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Using Weak Supervision and Data Augmentation in Question Answering—0
Learning to Transform for Generalizable Instance-wise InvarianceCode0
Improving Equivariance in State-of-the-Art Supervised Depth and Normal PredictorsCode1
Label Augmentation Method for Medical Landmark Detection in Hip Radiograph ImagesCode0
Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation—0
Enhancing Sharpness-Aware Optimization Through Variance SuppressionCode1
Graph-level Representation Learning with Joint-Embedding Predictive ArchitecturesCode1
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection—0
Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness—0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Robust Stance Detection: Understanding Public Perceptions in Social Media—0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan—0
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs—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